Vehicular traffic alerts for avoidance of abnormal traffic conditions

ABSTRACT

Methods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/241,769 (filed Aug. 19, 2016), which claims the benefit of U.S.Provisional Application No. 62/211,337 (filed on Aug. 28, 2015); U.S.Provisional Application No. 62/262,671 (filed on Dec. 3, 2015); U.S.Provisional Application No. 62/296,839 (filed on Feb. 18, 2016); U.S.Provisional Application No. 62/367,460 (filed on Jul. 27, 2016); U.S.Provisional Application No. 62/367,466 (filed on Jul. 27, 2016); U.S.Provisional Application No. 62/367,467 (filed on Jul. 27, 2016); U.S.Provisional Application No. 62/367,470 (filed on Jul. 27, 2016); U.S.Provisional Application No. 62/367,474 (filed on Jul. 27, 2016); U.S.Provisional Application No. 62/367,479 (filed on Jul. 27, 2016); U.S.Provisional Application No. 62/369,531 (filed on Aug. 1, 2016); U.S.Provisional Application No. 62/369,537 (filed on Aug. 1, 2016); U.S.Provisional Application No. 62/369,552 (filed on Aug. 1, 2016); U.S.Provisional Application No. 62/369,563 (filed on Aug. 1, 2016); and U.S.Provisional Application No. 62/369,577 (filed on Aug. 1, 2016). Theentirety of each of the foregoing applications is incorporated byreference herein.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to vehicle and pedestriansafety, and, more particularly, to generating and collecting data at aconnected vehicle, and using the data and/or vehicle-to-other device(V2x) wireless communication (e.g., vehicle-to-vehicle orvehicle-to-infrastructure) and data transmission to facilitate safervehicle travel and/or provide auto insurance cost savings to consumers.

BACKGROUND

Conventional telematics devices may collect certain types of dataregarding vehicle operation. However, conventional telematics devicesand data gathering techniques may have several drawbacks. Specifically,conventional telematics devices only monitor the movement and operatingstatus of the vehicle in which they are disposed. Such data is limitedto determining the vehicle location, whether the vehicle has been in anaccident, or similar simple information regarding the vehicle.

BRIEF SUMMARY

In one aspect, an electronic message is sent from a vehicle to a nearbyvehicle to alert the nearby vehicle that an abnormal traffic conditionhas occurred in the vehicle's operating environment. Various aspects mayinclude detecting that an abnormal traffic condition exists in anoperating environment of a vehicle and generating an electronic messageregarding the abnormal traffic condition. The electronic message maythen be transmitted via the vehicle's transceiver using a wirelesscommunication to the nearby vehicle to alert the nearby vehicle of theabnormal traffic condition and to allow the nearby vehicle to avoid theabnormal traffic condition. Examples of an abnormal traffic conditionmay include an erratic vehicle, an erratic driver, road construction, aclosed highway exit, slowed or slowing traffic, slowed or slowingvehicular congestion, or one or more other vehicles braking ahead of thevehicle. The abnormal traffic condition may also be bad weather and theelectronic message can be used to indicate a GPS location of the badweather. An abnormal traffic condition may be detected by analyzingvehicular telematics data. In some embodiments, an alternate route maybe generated to allow a nearby vehicle to avoid the abnormal trafficcondition. In other embodiments, an auto insurance discount may begenerated that is associated with the vehicle.

The vehicle may include one or more processors, which can bevehicle-mounted sensors or vehicle-mounted processors. In someembodiments, transmitting the electronic message to a nearby vehicle mayrequire transmitting the electronic message to a one or more remoteprocessors. The nearby vehicle can also be any of an autonomous vehicle,a semi-autonomous vehicle or a self-driving vehicle, in which each ofthe vehicles includes one or more processors for receiving thetransmitted electronic message. A nearby vehicle may also be a vehicleat a location that is in a direction of travel to the operatingenvironment of the vehicle.

In other aspects, telematics data and/or geographic location data may becollected, monitored, measured, and/or generated by one or morecomputing devices associated with a vehicle. The telematics data mayinclude various metrics that indicate the direction, speed,acceleration, braking, cornering, and/or motion of the vehicle in whichthe data is associated. The geographic location data may include ageographic location of the vehicle, such as latitude and longitudecoordinates, for example. The one or more computing devices may includesmart vehicle controller, vehicle central computer, an on-board computerintegrated within the vehicle, a mobile device, and/or a combination ofthese devices working in conjunction with one another. The one or morecomputing devices may broadcast the telematics data and/or thegeographic location data to one or more other devices via V2xcommunication, such as to other vehicles, infrastructure, remoteservers, or mobile devices, including mobile devices of other drivers,pedestrians and/or cyclists.

The telematics data and/or the geographic location data may be receivedand/or processed by one or more other computing devices to determinewhether an anomalous condition exists, such as a traffic accident, forexample. These one or more other computing devices may be vehiclecomputing devices, external computing devices (e.g., a remote server),another mobile computing device, a smart traffic infrastructure device(e.g., a smart traffic light), etc. If an anomalous condition isdetected, the geographic location of the vehicle associated with thetelematics data may be used as a condition to decide whether to generatean alert at (or send an alert notification to) the one or more othercomputing devices associated with nearby vehicles.

The telematics, location, and/or other data collected or generated by aconnected vehicle may be used for various purposes. The data may be usedby an insurance provider to generate auto insurance discount and/or riskaverse profiles based upon average or typical vehicle travelenvironment. The data collected may be used for accident reconstructionand/or accident cause determination. The present embodiments may alsoentail electric or hybrid vehicle battery conservation. The datacollected may be used to generate vehicle-usage profiles that moreaccurately reflect vehicle risk, or lack thereof, and facilitate moreappropriate auto insurance pricing. The data collected may be used togenerate a traffic condition broadcast that is broadcasted to nearbyvehicles or smart infrastructure via V2x (such as Vehicle-to-Vehicle,Vehicle-to-Infrastructure or Vehicle-to-Person) wireless communication.Individual consumers may also collected their own telematics data, andthen share their data when they choose with various merchants, such asrental car companies, to get discounted pricing on products or services.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred embodiments whichhave been shown and described by way of illustration. As will berealized, the present embodiments may be capable of other and differentembodiments, and their details are capable of modification in variousrespects. Accordingly, the drawings and description are to be regardedas illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates a block diagram of an exemplary telematics collectionsystem in accordance with an exemplary aspect of the present disclosure;

FIG. 2 illustrates a block diagram of an exemplary system that collectstelematics and/or other data, and uses V2x wireless communication tobroadcast the data collected to other vehicles, mobile devices, remoteservers, and smart infrastructure in accordance with an exemplary aspectof the present disclosure; and

FIG. 3 illustrates a block diagram of an exemplary computing device inaccordance with an exemplary aspect of the present disclosure.

FIG. 4 illustrates a flow diagram of an exemplary risk assessmentmethod.

FIG. 5 illustrates a flow diagram of an exemplary accident analysismethod.

FIG. 6 illustrates a flow diagram of an exemplary anomalous conditionmonitoring method.

FIG. 7 illustrates a flow diagram of an exemplary anomalous conditionanalysis method.

FIG. 8 illustrates a flow diagram of an exemplary battery conservationmethod.

FIG. 9 illustrates a flow diagram of an exemplary vehicle-usage profilegeneration method.

FIG. 10 illustrates a flow diagram of an exemplary vehicle-usage profilegeneration method.

FIG. 11 illustrates a flow diagram of an exemplary traffic conditionbroadcast method.

FIG. 12 illustrates a flow diagram of an exemplary alert generation andpresentation method.

FIG. 13 illustrates a flow diagram of an exemplary personal telematicsdata profile generation method.

FIG. 14 illustrates a flow diagram of an exemplary shared vehicle usagemonitoring method.

FIG. 15 illustrates a flow diagram of an exemplary driver evaluationmethod in accordance.

FIG. 16 illustrates a flow diagram of an exemplary vehicle alert method.

FIG. 17 illustrates a flow diagram of an exemplary pedestrian warningmethod.

The Figures depict preferred embodiments for purposes of illustrationonly. Alternative embodiments of the systems and methods illustratedherein may be employed without departing from the principles of theinvention described herein.

DETAILED DESCRIPTION

The present embodiments relate to, inter alia, determining whether ananomalous condition is detected at the location of a vehicle using oneor more computing devices within or otherwise associated with thevehicle. If the detected anomalous condition may impact or affectanother vehicle on the road, embodiments are described to generateand/or send alert notifications to other vehicles that may be soaffected. As further described throughout the disclosure, the process ofdetecting anomalous conditions and whether they apply to other vehiclesmay be performed through an analysis of geographic location data and/ortelematics data broadcasted from one or more computing devices within orotherwise associated with one or more respective vehicles.

The present embodiments may relate to collecting, transmitting, and/orreceiving telematics data; and may include a mobile device, avehicle-mounted processor, computer server, web pages, applications,software modules, user interfaces, interactive display screens, memoryunits, and/or other electronic, electrical, and/or wirelesscommunication equipment configured to provide the functionalitydiscussed herein. As compared with the prior art, the presentembodiments include specifically configured computing equipment thatprovide for an enhanced method of collecting telematics and/or othervehicle/driving conditions related data, and performing certain actionsbased upon the data collected. Using the telematics and/or other datacollected, in conjunction with the novel techniques discussed herein,recommendations and/or travel/driving guidance may be provided to remotevehicles and/or drivers.

The present embodiments may solve one or more technical problems relatedto (1) vehicle safety, and/or (2) vehicle navigation by using solutionsor improvements in another technological field, namely telematics.Vehicle safety and vehicle navigation is often impacted by short-termtraffic events that occur with little or no warning. For instance,vehicle accidents may be caused by road construction, other vehicleaccidents, traffic being temporarily re-routed, unexpected bad weather,other drivers or vehicles, etc.

To address these and other problems, telematics data (and/or driverbehavior or vehicle information) may be captured in real-time, or nearreal-time, by a computing device, such as a vehicle-mounted computer,smart vehicle controller, or a mobile device of a vehicle driver (orpassenger). The computing device may be specifically configured forgathering, collecting, and/or generating telematics and/or other data asa vehicle is traveling.

For instance, the vehicle-mounted computer or the mobile device may beequipped with (i) various sensors and/or meters capable of generatingtelematics data (GPS unit, speed sensor, speedometer, odometer,gyroscope, compass, accelerometer, etc.) and/or (ii) an application,such as a Telematics Data Application or Telematics “App,” that includescomputer instructions and/or software modules stored in a non-transitorymemory unit that control collecting and generating telematics and/orother data. The computing device and/or the application (or TelematicsApp) may provide a software module, user interface, and/or interactivedisplay screen configured to facilitate the data collection. Thecomputing device and/or Telematics App executing thereon may beconfigured to prepare or otherwise format the telematics and/or otherdata collected or generated for transmission (via wireless communicationand/or data transmission) to a mobile device of a second driver, aremote server, another (smart) vehicle, and/or smart infrastructure—allof which may be equipped with its own Telematics App or other telematicsrelated applications. The Telematics App may include otherfunctionality, including the mobile device functionality discussedelsewhere herein.

Alternatively, the computing device may remotely access a web page, suchas via wireless communication with a remote server. The web page mayprovide the computing device with the functionality to collect thetelematics and/or other data as the vehicle is moving. Additionally oralternatively, the web page may allow the computing device to upload ortransmit data in real-time, or near real-time, to a mobile device of asecond driver, a remote server, smart infrastructure, and/or another(smart) vehicle.

Additionally or alternatively, a smart vehicle controller or processormay be configured with the same functionality as that of the computingdevice described above. For instance, a smart vehicle controller mayinclude an application, software module, or computer instructions thatprovide for the telematics and/or other data collection and generationfunctionality discussed herein. The smart vehicle controller may be inwired or wireless communication with various (“smart” or “dumb”)vehicle-mounted meters, sensors, and/or detectors, such as speedometers,speed sensors, compasses, gyros, accelerometers, etc. that collectand/or generate telematics data and/or other data detailing orassociated with vehicle operation, and/or driving or driver behavior.

In one aspect, by solving problems with collecting telematics dataand/or other data associated with driver behavior and/or vehicleoperation or performance, problems with vehicle navigation and/orvehicle operation may be resolved. For instance, telematics dataassociated with a first vehicle may be collected in real-time by a firstvehicle computer or a mobile device of a first driver. The first vehiclemay be specifically configured to gather or generate telematics and/orother driver/vehicle data in real-time as the first vehicle istraveling, such as via a Telematics App. If a traffic event isencountered, about to be encountered, and/or expected or anticipated tobe encountered by the vehicle as it travels (e.g., road construction;heavy traffic; congestion; bad weather conditions; unlawful, unexpectedor erratic operation of other vehicles; questionable or abnormal drivingbehavior of other drivers; irresponsible or overly aggressive drivers;un-attentive or tired drivers, etc.), the telematics (and/or data) datacollected may indicate such.

The computing device, such as a vehicle computer or a mobile device(and/or Telematics App) may be configured to identify the type oftraffic event and transmit the type of traffic event to other mobiledevices, a remote server, smart vehicles, and/or smart infrastructure.In one embodiment, a mobile device (and/or Telematics App) may be inwireless communication with a smart vehicle control system of thevehicle, and the smart vehicle control system may transmit thetelematics and/or other data, and/or any associated warnings, to aremote server, and/or roadside smart infrastructure or nearby mobiledevice or vehicles of other drivers (such as to conserve battery powerof the mobile device).

Alternatively, the mobile device (and/or Telematics App) may transmitthe telematics and/or other data collected via wireless communicationand/or data transmission to a second computing device—such as a secondmobile device (or another driver), a second and smart vehicle, a remoteserver, and/or road side infrastructure (smart street signs or roadposts, smart toll booths, etc.). After which, the second and remotecomputing device may analyze the telematics and/or other data that iscollected in real-time, or near real-time, to determine traffic eventsin real-time, or near real-time, respectively. Based upon the type andextent of traffic event detected, the second computing device may issuewarnings, determine recommendations, and/or re-route vehicles. Forinstance, the second computing device may cause a display screen or userinterface of a mobile device or smart vehicle controller of remotedrivers to display a map with (1) a current route that the vehicle ison, (2) a virtual representation of the traffic event, and/or (3) analternate or recommended new route to an original destination thatavoids the traffic event.

An insurance provider may collect an insured's usage of the vehiclesafety functionality provided herein, such as at an insurance providerremote server and/or via a mobile device application. Based upon anindividual's usage and/or taking travel recommendations, such as travelrecommendations that reduce or lower risk and/or enhance driver orvehicle safety, insurance policies (such as vehicle or life insurancepolicies) may be adjusted, generated, and/or updated. The insuranceprovider remote server may calculate, update, and/or adjust insurancepremiums, rates, discounts, points, programs, etc., such as adjusting aninsurance discount or premium based upon the insured having thefunctionality discussed herein and/or the amount that the insured usesthe functionality discussed herein. The updated insurance policies(and/or premiums, rates, discounts, etc.) may be communicated toinsurance customers for their review, modification, and/or approval—suchas via wireless communication or data transmission from a remote serverto a mobile device or the insured.

Telematics and Vehicle Navigation

In one aspect, by solving problems with collecting telematics dataand/or other data associated with driver behavior and/or vehicleoperation or performance, problems with vehicle navigation and/orvehicle operation may be resolved. For instance, telematics dataassociated with a first vehicle may be collected in real-time by vehiclecomputer or a mobile device of a first driver. The computing device maybe specifically configured to gather or generate telematics and/or otherdriver/vehicle data in real-time as the vehicle is traveling. If atraffic event is encountered, about to be encountered, and/or expectedor anticipated to be encountered by the vehicle as it travels (e.g.,road construction; heavy traffic; congestion; bad weather conditions;unlawful, unexpected or erratic operation of other vehicles;questionable or abnormal driving behavior of other drivers;irresponsible or overly aggressive drivers; un-attentive or tireddrivers, etc.), the telematics (and/or other) data collected mayindicate such.

The computing device itself may be configured to identify the type oftraffic event and transmit the type of traffic event to mobile devices,a remote server, smart vehicles, and/or smart infrastructure. In oneembodiment, a mobile device collecting telematics data may be inwireless communication with a smart vehicle control system of thevehicle, and the smart vehicle control system may transmit thetelematics and/or other data, and/or any associated warnings, to aremote server, and/or roadside smart infrastructure or nearby mobiledevice or vehicles of other drivers (such as to conserve battery powerof the mobile device).

Additionally or alternatively, the computing device (e.g., vehicleprocessor, mobile device, or conventional telematics device) maytransmit the telematics and/or other data collected via wirelesscommunication and/or data transmission to a second computing device—suchas a second mobile device (or another driver), a second and smartvehicle, a remote server, and/or road side infrastructure (smart streetsigns or road posts, smart toll booths, etc.). After which, the secondand remote computing device may analyze the telematics and/or other datathat is collected in real-time, or near real-time, to determine trafficevents in real-time, or near real-time, respectively. Based upon thetype and extent of traffic event detected, the second computing devicemay issue warnings, determine recommendations, and/or re-route vehicles.For instance, the second computing device may cause a display screen oruser interface of a mobile device or smart vehicle controller of remotedrivers to display a map with (1) a current route that the vehicle ison, (2) a virtual representation of the traffic event, and/or (3) analternate or recommended new route to an original destination thatavoids the traffic event.

Exemplary Telematics Collection System

FIG. 1 illustrates a block diagram of an exemplary telematics collectionsystem 100 in accordance with an exemplary aspect of the presentdisclosure. In some aspects, telematics collection system 100 mayinclude hardware and software applications configured to measure,calculate, generate, and/or collect geographic location data and/ortelematics data indicative of the speed, direction, and/or motion ofvehicle 108. Additionally or alternatively, telematics collection system100 may include hardware and software applications configured to receiveand process geographic location data and/or telematics data sent fromanother telematics collection system, to determine whether an anomalouscondition has been detected, whether to generate an alert, and/orwhether to send an alert notification. Telematics collection system 100may include various data communication channels for facilitating datacommunications between the various hardware and software componentsand/or communications with one or more external components.

To accomplish this, telematics collection system 100 may include anysuitable number of computing devices, such as mobile computing device110 and/or on-board computing device 114, for example. These computingdevices may be disposed within vehicle 108, permanently installed invehicle 108, or removably installed in vehicle 108.

In the present aspects, mobile computing device 110 may be implementedas any suitable computing or mobile device, such as a mobile device(e.g., smartphone, tablet, laptop, wearable electronics, phablet, pager,personal digital assistant (PDA), smart glasses, smart watch orbracelet, etc.), while on-board computer 114 may be implemented as ageneral-use on-board computer or processor(s) installed by themanufacturer of vehicle 108 or as an aftermarket modification to vehicle108, for example. In various aspects, mobile computing device 110 and/oron-board computer 114 may be a thin-client device configured tooutsource any suitable portion of processing via communications with oneor more external components.

On-board computer 114 may supplement one or more functions performed bymobile computing device 110 described herein by, for example, sendinginformation to and/or receiving information from mobile computing device110. Mobile computing device 110 and/or on-board computer 114 maycommunicate with one or more external components via links 112 and 118,respectively. Additionally, mobile computing device 110 and on-boardcomputer 114 may communicate with one another directly via link 116.

In one aspect, mobile computing device 110 may be configured withsuitable hardware and/or software (e.g., one or more applications,programs, files, etc.) to determine a geographic location of mobilecomputing device 110 and, hence, vehicle 108, in which it is positioned.Additionally or alternatively, mobile computing device 110 may beconfigured with suitable hardware and/or software to monitor, measure,generate, and/or collect one or more sensor metrics as part of thetelematics data. Mobile computing device 110 may be configured tobroadcast the geographic location data and/or the one or more sensormetrics to one or more external components.

In some aspects, the external components may include another mobilecomputing device substantially similar to or identical to mobilecomputing device 110. In accordance with such aspects, mobile computingdevice 110 may additionally or alternatively be configured to receivegeographic location data and/or sensor metrics broadcasted from anothermobile computing device, the details of which are further discussedbelow. Mobile computing device 110 may be configured to determine, uponreceiving the geographic location data and/or sensor metrics, whether ananomalous condition exists at the geographic location indicated by thegeographic location data. If so, mobile computing device 110 may beconfigured to generate one or more audio and/or video alerts indicativeof the determined anomalous condition.

On-board computer 114 may be configured to perform one or more functionsotherwise performed by mobile computing device 110. However, on-boardcomputer 114 may additionally be configured to obtain geographiclocation data and/or telematics data by communicating with one or morevehicle sensors that are integrated into vehicle 108. For example,on-board computer 114 may obtain geographic location data viacommunication with a vehicle-integrated global navigation satellitesystem (GNSS). To provide additional examples, on-board computer 114 mayobtain one or more metrics related to the speed, direction, and/ormotion of vehicle 108 via any number of suitable sensors, such asspeedometer sensors, braking sensors, airbag deployment sensors, crashdetection sensors, accelerometers, etc.

In one aspect, mobile computing device 110 and/or on-board computer 114may operate independently of one another to generate geographic locationdata and/or telematics data, to receive geographic location data and/ortelematics data broadcasted from another telematics collection system,to determine whether to generate one or more alerts, and/or to generateone or more alert notifications. In accordance with such aspects,telematics collection system 100 may include mobile computing device 110but not on-board computer 114, and vice-versa.

In other aspects, mobile computing device 110 and/or on-board computer114 may operate in conjunction with one another to generate geographiclocation data and/or telematics data, to receive geographic locationdata and/or telematics data broadcasted from another telematicscollection system, to determine whether to generate one or more alerts,and to generate one or more alert notifications. In accordance with suchaspects, telematics collection system 100 may include both mobilecomputing device 110 and on-board computer 114. Mobile computing device110 and on-board computer 114 may share any suitable portion ofprocessing between one another to facilitate the functionality describedherein.

Upon receiving notification alerts from another telematics collectionsystem, aspects include telematics collection system 100 generatingalerts via any suitable audio, video, and/or tactile techniques. Forexample, alerts may be generated via a display implemented by mobilecomputing device 110 and/or on-board computer 114. To provide anotherexample, a tactile alert system 120 (e.g., a seat that can vibrate) maybe configured to generate tactile alerts to a vehicle operator 106 whencommanded by mobile computing device 110 and/or on-board computer 114.To provide another example, audible alerts may be generated via aspeaker 122, which may be part of vehicle 108's integrated speakersystem, for example.

Although telematics collection system 100 is shown in FIG. 1 asincluding one mobile computing device 110 and one on-board computer 114,various aspects include telematics collection system 100 implementingany suitable number of mobile computing devices 110 and/or on-boardcomputers 114.

Exemplary Telematics Alert Notification System

FIG. 2 illustrates a block diagram of an exemplary alert notificationsystem 200 in accordance with an exemplary aspect of the presentdisclosure. In one aspect, alert notification system 200 may include anetwork 201, N number of vehicles 202.1-202.N and respective mobilecomputing devices 204.1-204.N, an external computing device 206, and/oran infrastructure component 208. In one aspect, mobile computing devices204 may be an implementation of mobile computing device 110, as shown inFIG. 1, while vehicles 202 may be an implementation of vehicle 108, alsoshown in FIG. 1. Each of vehicles 202.1 and 202.2 may have an associatedon-board computer, which is not shown in FIG. 2 for purposes of brevity,but may be an implementation of on-board computer 114, as shown inFIG. 1. Each of vehicles 202.1 and 202.2 may be configured for wirelessinter-vehicle communication, such as vehicle-to-vehicle (V2V) wirelesscommunication and/or data transmission.

Although alert notification system 200 is shown in FIG. 2 as includingone network 201, two mobile computing devices 204.1 and 204.2, twovehicles 202.1 and 202.2, one external computing device 206, and/or oneinfrastructure component 208, various aspects include alert notificationsystem 200 implementing any suitable number of networks 201, mobilecomputing devices 204, vehicles 202, external computing devices 206,and/or infrastructure components 208. For example, alert notificationsystem 200 may include a plurality of external computing devices 206 andmore than two mobile computing devices 204, any suitable number of whichbeing interconnected directly to one another and/or via network 201.

In one aspect, each of mobile computing devices 204.1 and 204.2 may beconfigured to communicate with one another directly via peer-to-peer(P2P) wireless communication and/or data transfer. In other aspects,each of mobile computing devices 204.1 and 204.2 may be configured tocommunicate indirectly with one another and/or any suitable device viacommunications over network 201, such as external computing device 206and/or infrastructure component 208, for example. In still otheraspects, each of mobile computing devices 204.1 and 204.2 may beconfigured to communicate directly and indirectly with one and/or anysuitable device, which may be concurrent communications orcommunications occurring at separate times.

Each of mobile computing devices 204.1 and 204.2 may be configured tosend data to and/or receive data from one another and/or via network 201using one or more suitable communication protocols, which may be thesame communication protocols or different communication protocols as oneanother. To provide an example, mobile computing devices 204.1 and 204.2may be configured to communicate with one another via a direct radiolink 203 a, which may utilize, for example, a Wi-Fi direct protocol, anad-hoc cellular communication protocol, etc. Furthermore, mobilecomputing devices 204.1 and 204.2 may be configured to communicate withthe vehicle on-board computers located in vehicles 202.1 and 202.1,respectively, utilizing a BLUETOOTH communication protocol (radio linknot shown).

To provide additional examples, mobile computing devices 204.1 and 204.2may be configured to communicate with one another via radio links 203 band 203 c by each communicating with network 201 utilizing a cellularcommunication protocol. As an additional example, mobile computingdevices 204.1 and/or 204.2 may be configured to communicate withexternal computing device 206 via radio links 203 b, 203 c, and/or 203e. Still further, one or more of mobile computing devices 204.1 and/or204.2 may also be configured to communicate with one or more smartinfrastructure components 208 directly (e.g., via radio link 203 d)and/or indirectly (e.g., via radio links 203 c and 203 f via network201) using any suitable communication protocols.

Mobile computing devices 204.1 and 204.2 may be configured to executeone or more algorithms, programs, applications, etc., to determine ageographic location of each respective mobile computing device (and thustheir associated vehicle) to generate, measure, monitor, and/or collectone or more sensor metrics as telematics data, to broadcast thegeographic data and/or telematics data via their respective radio links,to receive the geographic data and/or telematics data via theirrespective radio links, to determine whether an alert should begenerated based upon the telematics data and/or the geographic locationdata, to generate the one or more alerts, and/or to broadcast one ormore alert notifications.

Network 201 may be implemented as any suitable network configured tofacilitate communications between mobile computing devices 204.1 and/or204.2 and one or more of external computing device 206 and/or smartinfrastructure component 208. For example, network 201 may include oneor more telecommunication networks, nodes, and/or links used tofacilitate data exchanges between one or more devices, and mayfacilitate a connection to the Internet for devices configured tocommunicate with network 201. Network 201 may include any suitablenumber of interconnected network components that form an aggregatenetwork system, such as dedicated access lines, plain ordinary telephonelines, satellite links, cellular base stations, a public switchedtelephone network (PSTN), etc., or any suitable combination thereof.Network 201 may include, for example, a proprietary network, a securepublic internet, a mobile-based network, a virtual private network, etc.

In aspects in which network 201 facilitates a connection to theInternet, data communications may take place over the network 201 viaone or more suitable Internet communication protocols. For example,network 201 may be implemented as a wireless telephony network (e.g.,GSM, CDMA, LTE, etc.), a Wi-Fi network (e.g., via one or more IEEE802.11 Standards), a WiMAX network, a Bluetooth network, etc. Thus,links 203 a-203 f may represent wired links, wireless links, or anysuitable combination thereof.

In aspects in which mobile computing devices 204.1 and 204.2 communicatedirectly with one another in a peer-to-peer fashion, network 201 may bebypassed and thus communications between mobile computing devices 204.1and 204.2 and external computing device 206 may be unnecessary. Forexample, in some aspects, mobile computing device 204.1 may broadcastgeographic location data and/or telematics data directly to mobilecomputing device 204.2. In this case, mobile computing device 204.2 mayoperate independently of network 201 to determine whether an alertshould be generated at mobile computing device 204.2 based upon thegeographic location data and the telematics data. In accordance withsuch aspects, network 201 and external computing device 206 may beomitted.

However, in other aspects, one or more of mobile computing devices 204.1and/or 204.2 may work in conjunction with external computing device 206to generate alerts. For example, in some aspects, mobile computingdevice 204.1 may broadcast geographic location data and/or telematicsdata, which is received by external computing device 206. In this case,external computing device 206 may be configured to determine whether analert should be sent to mobile computing device 204.2 based upon thegeographic location data and the telematics data.

External computing device 206 may be configured to execute varioussoftware applications, algorithms, and/or other suitable programs.External computing device 206 may be implemented as any suitable type ofdevice to facilitate the functionality as described herein. For example,external computing device 206 may be implemented as a network server, aweb-server, a database server, one or more databases and/or storagedevices, or any suitable combination thereof. Although illustrated as asingle device in FIG. 2, one or more portions of external computingdevice 206 may be implemented as one or more storage devices that arephysically co-located with external computing device 206, or as one ormore storage devices utilizing different storage locations as a shareddatabase structure (e.g. cloud storage).

In some embodiments, external computing device 206 may be configured toperform any suitable portion of the processing functions remotely thathave been outsourced by one or more of mobile computing devices 204.1and/or 204.2. For example, mobile computing device 204.1 and/or 204.2may collect data (e.g., geographic location data and/or telematics data)as described herein, but may send the data to external computing device206 for remote processing instead of processing the data locally. Insuch embodiments, external computing device 206 may receive and processthe data to determine whether an anomalous condition exists and, if so,whether to send an alert notification to one or more mobile computingdevices 204.1 and 204.2.

Smart infrastructure component 208 may be configured to communicate withone or more devices directly and/or indirectly. For example, smartinfrastructure component 208 may be configured to communicate directlywith mobile computing device 204.2 via link 203.d and/or with mobilecomputing device 204.1 via links 203 b and 203 f utilizing network 201.To provide another example, smart infrastructure component 208 maycommunicate with external computing device 206 via links 203 e and 203 futilizing network 201.

Smart infrastructure component 208 may be implemented as any suitabletype of traffic infrastructure component configured to receivecommunications from and/or to send communications to other devices, suchas external computing devices 204.1, 204.2 and/or external computingdevice 206, for example. For example, smart infrastructure component 208may be implemented as a traffic light, a railroad crossing light, aconstruction notification sign, a roadside display configured to displaymessages, a billboard display, etc.

In some aspects, smart infrastructure component 208 may be configured toreceive geographic location data and/or telematics data from one or moreother devices and to process this data to determine whether an anomalouscondition has been detected and whether the detected anomalous conditionsatisfies a threshold distance condition with respect to smartinfrastructure component 208. The threshold distance condition mayinclude, for example, the geographic location of the anomalous conditionbeing within a threshold radius of smart infrastructure component 208,on the same road serviced by smart infrastructure component 208, etc. Ifso, smart infrastructure component 208 may perform one or more relevantactions such as displaying one or more relevant messages to notifydrivers in the vicinity, to modify traffic patterns, to change trafficlight timing, to redirect traffic, etc.

In other aspects, smart infrastructure component 208 may receive dataindicating that an alert is to be generated and/or the type of alertthat is to be generated. In accordance with such aspects, one or more ofmobile computing devices 204.1, 204.2 and/or external computing device206 may make the determination of whether an anomalous condition existsand is within a threshold distance of smart infrastructure component208. If so, the data received by smart infrastructure component 208 maybe indicative of the type of anomalous condition, the location of theanomalous condition, commands to cause smart infrastructure component208 to perform one or more acts, the type of acts to perform, etc.

To provide some illustrative examples, if smart infrastructure component208 is implemented as a smart traffic light, smart infrastructurecomponent 208 may change a traffic light from green to red (orvice-versa) or adjust a timing cycle to favor traffic in one directionover another. To provide another example, if smart infrastructurecomponent 208 is implemented as a traffic sign display, smartinfrastructure component 208 may display a warning message that theanomalous condition (e.g., a traffic accident) has been detected aheadand/or on a specific road corresponding to the geographic location data.

Exemplary End-User/Destination Devices

The following details regarding the determination of an anomalouscondition are explained in this section with reference to computingdevice 300. In the present aspect, computing device 300 may beimplemented as any suitable computing device, such as a mobile computingdevice (e.g., mobile computing device 100, as shown in FIG. 1). Inanother aspect, computing device 300 may be implemented as an on-boardvehicle computer (e.g., on-board vehicle computer 114, as shown in FIG.1). In still other aspects, computing device 300 may be implemented as adevice external to a vehicle (e.g., remote computing device 206 or smartinfrastructure component 208, as shown in FIG. 2).

Depending upon the implementation of computing device 300, the methodsand processes utilized to determine the existence of anomalousconditions may be performed locally, remotely, or any suitablecombination of local and remote processing techniques.

FIG. 3 illustrates a block diagram of an exemplary computing device 300in accordance with an exemplary aspect of the present disclosure.Computing device 300 may be implemented as any suitable computing deviceconfigured to (1) monitor, measure, generate, and/or or collecttelematics and/or other data (including image, video, and/or audiodata); (2) broadcast the geographic location, telematics, and/or otherdata to one or more external components, such as via wirelesscommunication and/or data transmission; (3) receive geographic location,telematics, and/or other data broadcasted from another device, such asvia wireless communication and/or data transmission; (4) determinewhether an anomalous condition exists at the geographic locationindicated by the geographic location data based upon the telematicsand/or other data; (5) generate one or more alerts indicative of theanomalous condition; and/or (6) broadcast one or more alertnotifications to other devices, such as via wireless communicationand/or data transmission.

Computing device 300 may include a display 316, a graphics processingunit (GPU) 318, a location acquisition unit 320, a speaker/microphone322, a sensor array 326, a user interface 328, a communication unit 330,and/or a controller 340.

In one aspect, controller 340 may include a program memory 302, amicroprocessor (MP) 306, a random-access memory (RAM) 308, and/or aninput/output (I/O) interface 310, each of which may be interconnectedvia an address/data bus 312. Controller 340 may be implemented as anysuitable type and/or number of processors, such as a host processor forthe relevant device in which computing device 300 is implemented, forexample. In some aspects, controller 240 may be configured tocommunicate with additional data storage mechanisms that are not shownin FIG. 3 for purposes of brevity (e.g., one or more hard disk drives,optical storage drives, solid state storage devices, etc.) that residewithin or are otherwise associated with mobile computing device 200.

Program memory 302 may store data used in conjunction with one or morefunctions performed by computing device 300 to facilitate theinteraction between computing device 300 and one or more other devices.For example, if computing device 300 is implemented as a mobilecomputing device (e.g., mobile computing device 204.1, as shown in FIG.2), then program memory 302 may store one or more programs,applications, algorithms, etc. that, when executed by controller 340,facilitate the interaction between mobile computing device 204.1 and (i)one or more networks (e.g., network 201), (ii) other mobile computingdevices (e.g., mobile computing device 204.2), (iii) external computingdevices (e.g., external computing device 206), (iv) vehicles (e.g.,vehicle 108), (v) vehicle on-board computers (e.g., on-board computer114), infrastructure components (e.g., smart infrastructure component208), etc.

In various aspects, program memory 302 may be implemented as anon-transitory tangible computer readable media configured to storecomputer-readable instructions, that when executed by controller 340,cause controller 340 to perform various acts. Program memory 302 mayinclude an operating system 342, one or more software applications 344,and one or more software routines 352. To provide another example,program memory 302 may include other portions to store data that may beread from and written to by MP 306, such as data storage 360, forexample.

In one aspect, one or more MPs (micro-processors) 306 may be configuredto execute one or more of software applications 344, software routines352 residing in program memory 302, and/or other suitable softwareapplications. For example, operating system 342 may be implemented asany suitable operating system platform depending upon the particularimplementation of computing device 300. For example, if computing device300 is implemented as a mobile computing device, operating system 342may be implemented as a mobile OS platform such as the iOS®, Android™,Palm® webOS, Windows® Mobile/Phone, BlackBerry® OS, or Symbian® OSmobile technology platforms, developed by Apple Inc., Google Inc., PalmInc. (now Hewlett-Packard Company), Microsoft Corporation, Research inMotion (RIM), and Nokia, respectively.

In one embodiment, data storage 360 may store data such as applicationdata for the one or more software applications 344, routine data for theone or more software routines 352, geographic location data and/ortelematics data, etc.

Display 316 may be implemented as any suitable type of display and mayfacilitate user interaction with computing device 300 in conjunctionwith user interface 328. For example, display 316 may be implemented asa capacitive touch screen display, a resistive touch screen display,etc. In various embodiments, display 316 may be configured to work inconjunction with controller 340 and/or GPU 318 to display alerts and/ornotifications received from other devices indicative of detectedanomalous conditions.

Communication unit 330 may be configured to facilitate communicationsbetween computing device 300 and one or more other devices, such asother vehicle computing devices, other mobile computing devices,networks, external computing devices, smart infrastructure components,etc. As previously discussed with reference to FIGS. 1 and 2, computingdevice 300 may be configured to communicate with these other devices inaccordance with any suitable number and type of communication protocols.Thus, in various aspects, communication unit 330 may be configured tosupport any suitable number and type of communication protocols basedupon a particular network and/or device in which computing device 300 iscommunicating to facilitate this functionality.

Communication unit 330 may be configured to support separate orconcurrent communications, which may be the same type of communicationprotocol or different types of communication protocols. For example,communication unit 330 may be configured to facilitate communicationsbetween computing device 300 and an external computing device (e.g.,external computing device 206) via cellular communications whilefacilitating communications between computing device 300 and the vehiclein which it is carried (e.g., vehicle 108) via BLUETOOTH communications.

Communication unit 330 may be configured to broadcast data and/or toreceive data in accordance with any suitable communications schedule.For example, communication unit 330 may be configured to broadcastgeographic location data and/or telematics data every 15 seconds, every30 seconds, every minute, etc. As will be further discussed below, thegeographic location data and/or telematics data may be sampled inaccordance with any suitable sampling period. Thus, when broadcasted bycommunications unit 330 in accordance with a recurring schedule, thegeographic location data and/or telematics data may include a log orcollection of the geographic location data and/or telematics data thatwas sampled since the last data transmission. A suitable communicationschedule may be selected as a tradeoff between a desired anomalouscondition detection speed and battery usage of computing device 300,when applicable.

Additionally or alternatively, aspects include communication unit 330being configured to conditionally send data, which may be particularlyadvantageous when computing device 300 is implemented as a mobilecomputing device, as such conditions may help reduce power usage andprolong battery life. For example, communication unit 330 may beconfigured to only broadcast when telematics data has been sampled sincethe last transmission, which will be further discussed below withregards to sensor array 326. Controller 340 may determine whether datahas been sampled since the last transmission by, for example, analyzinga memory address range (e.g., in data storage 360, RAM 308, etc.)associated with the storage of the telematics data and comparing thecontents of this buffer to a known range of valid values.

To provide another example, aspects include communication unit 330 beingadditionally or alternatively configured to only broadcast telematicsdata when computing device 300 is connected to a power source (e.g., anin-vehicle charger). To provide still another example, aspects includecommunication unit 330 being additionally or alternatively configured toonly broadcast telematics data when communication unit 330 is connectedto and/or communicating with a device identified as a vehicle. This mayinclude, for example, identifying a BLUETOOTH connection as a validvehicle connection to satisfy this condition upon installation and/orsetup of the relevant application or program executed by computingdevice 300 to facilitate the functionality described herein.

Location acquisition unit 320 may be configured to generate geographiclocation data utilizing any suitable global positioning techniques. Forexample, location acquisition unit 320 may communicate with one or moresatellites and/or wireless transmitters to determine a location ofcomputing device 300. Location acquisition unit 320 may use “AssistedGlobal Positioning System” (A-GPS), satellite GPS, or any other suitableglobal positioning protocol (e.g., the GLONASS system operated by theRussian government, the Galileo system operated by the European Union,etc.) to determine a geographic location of computing device 300.

In one aspect, location acquisition unit 320 may periodically store oneor more geographic locations of computing device 300 as geographiclocation data in any suitable portion of memory utilized by computingdevice 300 (e.g., program memory 302, RAM 308, etc.) and/or to anotherdevice (e.g., another mobile computing device, an external computingdevice, etc.). In this way, location acquisition unit 320 may sample thelocation of computing device 300 in accordance with any suitablesampling rate (e.g., every 5 seconds, 10 seconds, 30 seconds, etc.) andstore this geographic location data representing the position ofcomputing device 300, and thus the vehicle in which it is travelling,over time.

Speaker/microphone 322 may be configured as one or more separatedevices. Speaker/microphone 322 may include a microphone configured todetect sounds and to convert sounds to data suitable for communicationsvia communications unit 330. Speaker/microphone 322 may additionally oralternatively include a speaker configured to play sound in response todata received from one or more components of computing device 300 (e.g.,controller 340). In one embodiment, speaker/microphone 322 may beconfigured to play audible alerts.

User-interface 328 may be implemented as any suitable device configuredto collect user input, such as a “soft” keyboard displayed on display316 of computing device 300, a keyboard attached to computing device300, an external keyboard communicating via a wired or a wirelessconnection (e.g., a BLUETOOTH keyboard), an external mouse, etc.

Sensor array 326 may be configured to measure any suitable number and/ortype of sensor metrics as part of the telematics data. In one aspect,sensor array 326 may be implemented as one or more sensors positioned todetermine the speed, force, heading, and/or direction associated withmovements of computing device 300 and, thus, a vehicle in whichcomputing device 300 is positioned. Additionally or alternatively,sensor array 326 may be configured to communicate with one or moreportions of computing device 300 to measure, collect, and/or generateone or more sensor metrics from one or more non-sensor sources, whichwill be further discussed below.

To generate one or more sensor metrics, sensor array 326 may include,for example, one or more cameras, accelerometers, gyroscopes,magnetometers, barometers, thermometers, proximity sensors, lightsensors, Hall Effect sensors, audio or video recorders, etc. In aspectsin which sensor array 326 includes one or more accelerometers, sensorarray 326 may be configured to measure and/or collect accelerometermetric values utilizing an X-axis, Y-axis, and Z-axis accelerometer. Inaccordance with such aspects, sensor array 326 may measure sensor metricvalues as a three-dimensional accelerometer vector that represents themovement of computing device 300 in three dimensional space by combiningthe outputs of the X-axis, Y-axis, and Z-axis accelerometers using anysuitable techniques.

In various aspects, sensor array 326 may be configured to sample the oneor more sensor metrics in accordance with any suitable sampling rateand/or based upon one or more conditions being satisfied. For example,sensor array 326 may be configured to implement one or moreaccelerometers to sample sensor metrics indicative of a g-forceassociated with vehicle braking, acceleration, and cornering at a rateof 15 Hz, 30 Hz, 60 Hz, etc., which may be the same sampling rate as oneanother or different sampling rates. To provide another example, sensorarray 326 may be configured to implement one or more gyroscopes toimprove the accuracy of the measured one or more sensor metrics and todetermine whether the phone is in use or stationary within a vehicle. Toprovide another example, sensor array 326 may implement a compass(magnetometer) to determine a direction or heading of a vehicle in whichcomputing device 300 is located.

Again, sensor array 326 may additionally or alternatively communicatewith other portions of computing device 300 to obtain one or more sensormetrics even though these sensor metrics may not be measured by one ormore sensors that are part of sensor array 326. For example, sensorarray 326 may communicate with one or more of location acquisition unit320, communication unit 330, and/or controller 340 to obtain data suchas timestamps synchronized to the sampling of one or more sensor metrics(which may be measured to within hundredths of a second or smallerresolutions), geographic location data (and correlated timestampsthereof), a velocity based upon changes in the geographic location dataover time, a battery level of computing device 300, whether a battery ofcomputing device 300 is charging, whether computing device 300 is beinghandled or otherwise in use, an operating status of computing device 300(e.g., whether computing device 300 is unlocked and thus in use).

In one aspect, sensor array 326 may sample one or more sensor metricsbased upon one or more conditions being satisfied. For example, sensorarray 326 may determine, based upon gyroscope sensor metrics,communication with controller 340, etc., whether computing device 300 isin use. If computing device 300 is in use (e.g., when implemented as amobile computing device) then the movement of computing device 300within the vehicle may not truly represent the vehicle motion, therebycausing sensor metrics sampled during this time to be erroneous.Therefore, aspects include sensor array 326 sampling the one or moresensor metrics when computing device 300 is not in use, and otherwisenot sampling the one or more sensor metrics.

In one aspect, sensory array 326 may include one or more cameras and/orimage capture devices. When sensory array 326 is implemented with one ormore cameras, these cameras may be configured as any suitable type ofcamera configured to capture and/or store images and/or video. Forexample, when mobile computing device 300 is mounted in a vehicle, thecamera may be configured to store images and/or video data of the roadin front of the vehicle in which it is mounted, and to store this datato any suitable portion of program memory 302 (e.g., data storage 360).Controller 340 and/or MP 306 may analyze this data to generate one ormore local alerts, to transmit signals indicative of detected alters toone or more other devices, etc., which is further discussed below withreference to the execution of anomalous condition detection routine 358.

Again, the telematics data broadcasted by computing device 300 mayinclude one or more sensor metrics. However, the telematics data mayadditionally or alternatively include other external data that may berelevant in determining the presence of an anomalous condition. Forexample, the telematics data may include external data such as speedlimit data correlated to a road upon which computing device 300 islocated (and thus the vehicle in which it is travelling), an indicationof a type of road, a population density corresponding to the geographiclocation data, etc.

In some aspects, computing device 300 may obtain this external data byreferencing the geographic location data to locally stored data (e.g.,data stored in data storage 360) and broadcasting this data appended toor otherwise included with the sensor metrics data as part of thetelematics data. In other aspects, the device receiving the telematicsdata (e.g., a mobile computing device, an external computing device, aninfrastructure component) may generate the external data locally or viacommunications with yet another device. As will be further discussedbelow, this external data may further assist the determination ofwhether an anomalous condition is present.

In some aspects, software applications 344 and/or software routines 352may reside in program memory 302 as default applications that may bebundled together with the OS of computing device 300. For example, webbrowser 348 may be part of software applications 344 that are includedwith OS 342 implemented by computing device 300.

In other aspects, software applications 344 and/or software routines 352may be installed on computing device 300 as one or more downloads, suchas an executable package installation file downloaded from a suitableapplication store via a connection to the Internet. For example, alertnotification application 346, telematics collection routine 354,geographic location determination routine 356, and/or anomalouscondition detection routine 358 may be stored to suitable portions ofprogram memory 302 upon installation of a package file downloaded insuch a manner. Examples of package download files may include downloadsvia the iTunes® store, the Google Play® store, the Windows Phone® store,downloading a package installation file from another computing device,etc. Once downloaded, alert notification application 346 may beinstalled on computing device 300 as part of an installation packagesuch that, upon installation of alert notification application 346,telematics collection routine 354, geographic location determinationroutine 356, and/or anomalous condition detection routine 358 may alsobe installed.

In one embodiment, software applications 344 may include an alertnotification application 346, which may be implemented as a series ofmachine-readable instructions for performing the various tasksassociated with executing one or more embodiments described herein. Inone aspect, alert notification application 346 may cooperate with one ormore other hardware or software portions of computing device 300 tofacilitate these functions.

To provide an illustrative example, alert notification application 344may include instructions for performing tasks such as determining ageographic location of computing device 300 (e.g., via communicationswith location acquisition unit 330), monitoring, measuring, generating,and/or collecting telematics data, broadcasting the geographic locationdata and/or the telematics data to one or more external devices,receiving geographic location data and/or telematics data from anothercomputing device, determining whether an anomalous condition existsbased upon the geographic location data and/or the telematics data,generating one or more alerts indicative of the determined anomalouscondition, receiving user input, facilitating communications betweencomputing device 300 and one or more other devices in conjunction withcommunication unit 330, etc.

Software applications 344 may include a web browser 348. In someembodiments (e.g., when computing device 300 is implemented as a mobilecomputing device), web browser 348 may be a native web browserapplication, such as Apple's Safari®, Google Chrome™ mobile web browser,Microsoft Internet Explorer® for Mobile, Opera Mobile™, etc. In otherembodiments, web browser 348 may be implemented as an embedded webbrowser.

Regardless of the implementation of web browser 348, various aspectsinclude web browser 348 being implemented as a series ofmachine-readable instructions for interpreting and displaying web pageinformation received from an external computing device (e.g., externalcomputing device 204.2, as shown in FIG. 2). This web page informationmay be utilized in conjunction with alert notification application 346to perform one or more function of the aspects as described herein.

In one embodiment, software routines 352 may include a telematicscollection routine 354. Telematics collection routine 354 may includeinstructions, that when executed by controller 340, facilitate sampling,monitoring, measuring, collecting, quantifying, storing, encrypting,transmitting, and/or broadcasting of telematics data. In some aspects,telematics collection routine 354 may facilitate collection oftelematics data locally via one or more components of computing device300 (e.g., via sensor array 326, location acquisition unit 320,controller 340, etc.). In other aspects, telematics collection routine354 may facilitate the storage of telematics data received from anotherdevice (e.g., via communication unit 330). Such other devices mayinclude external computing devices 206 (e.g., remote servers),infrastructure components 208 (e.g., smart traffic signals, smart tollbooths, embedded sensors within roadways or bridges, etc.), oradditional sensors disposed within the vehicle 108 (e.g., an aftermarketdashboard camera, a built-in forward proximity sensor, etc.).

In one embodiment, software routines 352 may include a geographiclocation determination routine 356. Geographic location determinationroutine 356 may include instructions, that when executed by controller340, facilitate sampling, measuring, collecting, quantifying, storing,transmitting, and/or broadcasting of geographic location data (e.g.,latitude and longitude coordinates, and/or GPS data). In some aspects,geographic location determination routine 356 may facilitate generatingand/or storing geographic location data locally via one or morecomponents of computing device 300 (e.g., via location acquisition unit320 and/or communication unit 330). In other aspects, geographiclocation determination routine 356 may facilitate the storage ofgeographic location data received from another device (e.g., viacommunication unit 330).

Additionally or alternatively, software routines 352 may includeanomalous condition detection routine 358. Anomalous condition detectionroutine 358 may include instructions, that when executed by controller340, facilitate the determination of whether an anomalous conditionexists based upon the telematics data, the geographic location data,and/or image and/or video data captured by one or more cameras or otherimaging devices. An anomalous condition may include any suitablecondition that indicates a deviation from normal traffic patterns. Forexample, if an accident occurs, traffic may slow down due to a carpileup, a reduction in available lanes, and/or rerouting of traffic.Because the telematics data may include data indicative of the speedlimit at the location corresponding to the geographic location where thetelematics data was sampled, a comparison between the speed of computingdevice 300 and the posted or other speed limit data (such as acomparison between mobile device or vehicle speed with a map of, and/orknown, posted speed limit information) may indicate an anomalouscondition. Furthermore, because each vehicle may sample and/or broadcastgeographic location data and/or telematics data in real time, theanomalous conditions may be detected with minimal delay as they occur.

Although the speed of the vehicle may indicate an anomalous condition,aspects include other types of anomalous conditions being detected basedupon the telematics data. For example, an anomalous condition may beidentified when the one or more sensor metrics indicate that an airbaghas been deployed, and thus the vehicle associated with computing device300 has been in an accident. This may be determined, for example, via ananalysis of barometer readings matching a pressure versus time profileand/or via an indication from a dedicated airbag deployment sensorlocated in the vehicle.

To provide another example, an anomalous condition may be identifiedbased upon weather fluctuations associated with a rapid formation ofice, a sudden change from a paved to a dirt road, the triggering of acrash detection system, a threshold number of wheel slips and/or skidsbeing sampled within a threshold sampling period (indicating slipperyconditions), sensor metrics indicative of a rollover condition, a suddenstop (indicating a collision), a departure from the road (indicating apulled over vehicle), etc.

To provide an illustrative example based upon a traffic accident, if afirst vehicle carrying a first computing device 300 is slowed down dueto a traffic accident, then the one or more sensor metrics sampled bysensor array 326 will indicate the speed of the first vehicle over aperiod of time. If the one or more sensor metrics indicate that thefirst vehicle's speed is below the speed limit by some threshold amountor proportion thereof (e.g., 20 mph in a 55 mph zone, 50% of the postedspeed limit, etc.) and this is maintained for a threshold duration oftime (e.g., 30 seconds, one minute, two minutes, etc.) then controller340 may, upon execution of anomalous condition detection routine 358,conclude that an anomalous condition has been detected. This anomalouscondition may also be correlated to the geographic location associatedwith the geographic location data due to synchronization between thegeographic location data and the sampled telematics data.

Further continuing this example, upon determination of the anomalouscondition, alert notification application 346 may broadcast anotification indicating the detected anomalous condition, the telematicsdata, and/or the geographic location data associated with the detectedanomalous condition. In one aspect, a second vehicle equipped with asecond computing device 300 may receive this data and further determinewhether the anomalous condition is relevant based upon the geographicrelationship between the first and second devices, which is furtherdiscussed below. If the anomalous condition is relevant, then the secondcomputing device 300 may generate an alert indicating the anomalouscondition.

To provide another example by modifying the details of the previous one,aspects may include computing device 300 broadcasting telematics dataand/or geographic location data but not notification data. In accordancewith such aspects, upon being received by a second computing device 300(e.g., a mobile computing device in a second vehicle, an externalcomputing device, a smart infrastructure component, etc.) the secondcomputing device 300 may determine the relevance of the anomalouscondition based upon the geographic relationship between itself and thefirst computing device 300.

If the second computing device 300 determines that an anomalouscondition, even if present, would be irrelevant or inapplicable basedupon the distance between these devices or location relative to adirection of travel, the second computing device 300 may ignore thetelematics data, thereby saving processing power and battery life.However, if the second computing device 300 determines that thegeographic location data indicates a potentially relevant anomalouscondition, the second computing device 300 may further process thetelematics data and take the appropriate relevant action if an anomalouscondition is found (e.g., issue an alert notification, generate analert, display a warning message, etc.).

To provide yet another example by further modifying the details in theprevious two, aspects may include computing device 300 broadcasting thetelematics data and geographic location data to an external computingdevice (e.g., to external computing device 206 via network 201, as shownin FIG. 2). In addition, the second computing device 300 associated withthe second vehicle may likewise broadcast telematics data and geographiclocation data to the external computing device. In accordance with suchaspects, the external computing device may determine whether ananomalous condition exists and is relevant to each of the first andsecond devices 300 based upon a geographic relationship between thefirst and second devices 300. When relevant, external computing devicemay be configured to send alert notifications to the first and/or seconddevices 300, which may include any suitable type of communications suchas push notifications, a short messaging service (SMS) message, anemail, a notification that used in conjunction with the OS running oneach receptive computing device 300, etc. Upon receiving thenotification from the external computing device, the first and/or secondcomputing device 300 may generate an alert indicating a description ofthe anomalous condition and/or its location.

The geographic relationship between two or more devices 300 may beutilized in several ways to determine the relevance of the anomalouscondition. For instance, current speed, location, route, destination,and/or direction of travel of a first vehicle (collecting and/orassociated with the telematics data) may be individually or collectivelycompared with current speed, location, route, destination, and/ordirection of travel of a second vehicle traveling on the road. As oneexample of the geographic relationship, a first vehicle location (andassociated with a travel or traffic event) may be compared with a secondvehicle location, current route, and/or destination to determine whetherthe second vehicle should divert course or slow down to alleviate therisk of the second vehicle being involved in a collision or a trafficjam (as a result of the travel or traffic event that is identified bythe telematics data).

As another example of the geographic relationship, a radius from onevehicle or a line-of-sight distance between vehicles may be utilized andcompared to a threshold distance. For example, if computing device 300is implemented as an external computing device and determines aline-of-sight distance between a first and second vehicle to be lessthan a threshold distance (e.g., a half mile, one mile, etc.), then theexternal computing device may issue an alert notification to bothvehicles. In this way, an external computing device may act as an alertmanagement device, processing data and sending notifications to thosedevices for which a detected anomalous condition is relevant.

In another example of the geographic relationship, the geographiclocation data may be correlated with a map database to associate theanomalous condition with a road and to determine the relevance of theanomalous condition based upon other vehicles sharing the road. The mapdatabase may be stored, for example, in a suitable portion of computingdevice 300 (e.g., data storage 360) or retrieved via communications withone or more external computing devices. To provide an illustrativeexample, a computing device 300 may be implemented as an externalcomputing device. The external computing device may determine, fromtelematics data and geographic location data received from a firstcomputing device 300, that a first vehicle is located on a highway at acertain geographic location. If the external computing device determinesthat a second computing device 300 in a vehicle travelling on the samehighway is within a threshold distance approaching the first vehicle,then the external computing device may issue an alert notification tothe second vehicle.

In yet other aspects, the geographic location data may be correlatedwith a geofence database to determine the relevance of the anomalouscondition based upon whether other vehicles are located inside thegeofence. The geofence database may be stored, for example, in asuitable portion of computing device 300 (e.g., data storage 360) orretrieved via communications with one or more external computingdevices. To provide another illustrative example, a computing device 300may be implemented as an external computing device. The externalcomputing device may determine, from telematics data and geographiclocation data received from a first computing device 300, that a firstvehicle is located on a highway at a certain geographic location. Theexternal computing device may calculate a geofence having a shapesubstantially matching the road upon which the first vehicle istravelling.

The geofence may be calculated as having any suitable shape such thatthe appropriate vehicles are notified of the detected anomalouscondition. For example, the geofence shape may follow the contours ofthe road and extend ahead of the first vehicle and behind the firstvehicle some threshold distances, which may be the same or differentthan one another. To provide another example, the geofence shape mayinclude other arterial roads that feed into the road upon which thefirst vehicle is travelling, roads that branch off of the road uponwhich the first vehicle is travelling, roads anticipated to be impactedby the anomalous condition, etc.

In some aspects, the geofence may be adjusted or modified based upon achange in the location of computing device 300. This change may betriggered using any suitable data indicative of potentially increasingroad densities, such as changes in population density data associatedwith the geographic location of the computing device 300, changes in atype of road upon which computing device 300 is determined to betravelling, time of day, weather conditions, known risk levels of areasor road segments (e.g., high-risk intersections), etc. Similarly, thegeofence may be determined based upon speed at which the computingdevice 300 is travelling, a time-to-distance threshold, or other suchfactors. Any distance or other thresholds described herein may also besimilarly adjusted based upon such considerations.

For example, a first computing device 300 may be implemented as a mobilecomputing device and associated with a first vehicle, while a secondcomputing device 300 may be implemented as an external computing device.The external computing device may calculate an initial geofence as athreshold distance radius centered about the first vehicle's location.The geographic location data corresponding to the first vehicle'slocation may have associated population density data that is correlatedwith locally stored data or data retrieved by the external computingdevice. When the population density data surpasses a threshold densityvalue, the shape of the geofence may be adjusted from the radiuscentered about the first vehicle's location to include only the roadupon which the first vehicle is travelling. In this way, computingdevice 300 may prevent false alert notifications from being sent toother vehicles travelling in close proximity to the first vehicle, buton nearby roads unaffected by the detected anomalous condition.

Although FIG. 3 depicts controller 340 as including one program memory302, one MP 306, and one RAM 308, controller 340 may include anysuitable number of program memory 302, MP 306, and RAM 308. Furthermore,although FIG. 3 depicts controller 340 as having a single I/O interface310, controller 340 may include any suitable number and/or types of I/Ointerfaces 310. In various aspects, controller 340 may implement RAM(s)308 and program memories 302 as any suitable type of memory, such asnon-transitory computer readable memories, semiconductor memories,magnetically readable memories, and/or optically readable memories, forexample.

Insurance Applications

As noted herein, the present embodiments may be used to adjust, update,and/or generate insurance policies. Insurance policies, such as auto,usage-based, home, and/or household insurance policies, may be adjusted,updated, and/or generated for insureds or potential customers that havemobile devices and/or vehicles that are equipped or configured with oneor more of the functionalities discussed herein.

For instance, insureds or family members may have mobile devices and/ora connected vehicle that are configured to receive telematics dataassociated with other vehicles and/or abnormal road or travel conditionsthat other drivers are experiencing. The telematics may be receiveddirectly from other vehicles, or indirectly from smart infrastructureand/or insurance provider remote servers. As a result, the insuredsand/or their family members may be timely notified of traffic or travelevents and then may take alternate routes (or even not drive or delaydriving) to lower their risk of getting in an accident due to thetraffic or travel events. An insurance provider may promote or rewardsuch risk averse behavior and/or safer driving with lower insurancepremiums, rates, and/or increased discounts, such as for usage-based orother types of auto insurance.

Discounts & Risk Profile Based Upon Travel Environment

In one aspect, a computer-implemented method of providing auto insurancediscounts may be provided. The method may include (1) receiving, via oneor more processors (or associated transceivers), such as via wirelesscommunication or data transmission, telematics and/or other data from avehicle or a mobile device of an insured; (2) determining, via the oneor more processors, an average travel environment that the vehicletravels in, the average travel environment accounting for heavy or lightpedestrian traffic and/or heavy or light vehicle traffic that thevehicle typically travels in; (3) using, via the one or more processors,the average travel environment to build a risk averse profile for theinsured; (4) generating or updating, via the one or more processors, anauto insurance discount for the insured based upon their risk averseprofile; and/or (5) transmitting, via the one or more processors (orassociated transceivers), the auto insurance discount to the insured'svehicle or mobile device for display for the insured's review and/orapproval such that insurance discounts are provided based upon a riskassociated with the travel environment that an insured vehicle orinsured typically travels within. The method may include additional,less, or alternate actions, including those discussed elsewhere herein.

For instance, the telematics and/or other data may indicate or includeinformation detailing (i) an amount of pedestrian traffic, and/or (ii)the types of streets that the vehicle travels through on a daily orweekly basis, and the risk averse profile may reflect the amount ofpedestrian traffic and/or types of streets. The telematics and/or otherdata may indicate or include information detailing (i) an amount ofvehicle traffic, and/or (ii) the types of roads that the vehicle travelsthrough or in on a daily or weekly basis, and the risk averse profilemay reflect the amount of vehicle traffic and/or types of roads. Thetelematics and/or other data may be collected over one or more vehicletrips or days.

In another aspect, a computer-implemented method of providing autoinsurance discounts may be provided. The method may include (1)receiving, via one or more processors (or associated transceivers), suchas via wireless communication or data transmission, telematics and/orother data from a vehicle or a mobile device of an insured, thetelematics and/or other data indicative of a travel environment of thevehicle; (2) determining, via the one or more processors, a risk profilefor the vehicle that reflects a travel environment that the vehicletravels in, the travel environment accounting for pedestrian trafficand/or vehicle traffic that the vehicle typically travels in; (3)generating or updating, via the one or more processors, an autoinsurance discount for the insured or the vehicle based upon the riskprofile; and/or (4) transmitting, via the one or more processors (orassociated transceivers), the auto insurance discount to the insured'svehicle or mobile device for display for the insured's review and/orapproval such that insurance discounts are provided based upon a riskassociated with the travel environment that an insured vehicle orinsured typically travels within. The method may include additional,less, or alternate actions, including those discussed elsewhere herein.

For instance, the telematics and/or other data may indicate or includeinformation detailing (i) an amount of pedestrian traffic, and/or (ii)the types of streets/roads that the vehicle travels through on a dailyor weekly basis, and the risk profile may reflect the amount ofpedestrian traffic and/or types of streets. The telematics and/or otherdata may indicate or include information detailing (i) an amount ofvehicle traffic, and/or (ii) the types of roads that the vehicle travelsthrough or in on a daily or weekly basis, and the risk profile mayreflect the amount of vehicle traffic and/or types of roads. Thetelematics and/or other data may be collected over one or more vehicletrips or days, and may be associated with multiple drivers of thevehicle, and/or the telematics and/or other data may be used to identifythe driver driving the vehicle during each trip.

In another aspect, a computer-implemented method of providing autoinsurance discounts may be provided. The method may include (1)receiving, via one or more processors (or associated transceivers), suchas via wireless communication or data transmission, telematics and/orother data from a vehicle controller/processor or a mobile device of aninsured; (2) generating or building, via the one or more processors, atravel environment for the vehicle using or based upon the telematicsand/or other data, the travel environment accounting for pedestriantraffic and/or vehicle traffic that the vehicle typically travels in orwith; (3) generating or updating, via the one or more processors, anauto insurance discount for the insured or the vehicle based upon thetravel environment; and/or (4) transmitting, via the one or moreprocessors (or associated transceivers), the auto insurance discount tothe insured's vehicle or mobile device for display for the insured'sreview and/or approval such that insurance discounts are provided basedupon a risk associated with the travel environment that an insuredvehicle or insured typically travels within. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

For instance, the telematics and/or other data may indicate or includeinformation detailing (i) an amount of pedestrian traffic, and/or (ii)the types of streets/roads that the vehicle travels through on a dailyor weekly basis. The travel environment generated may reflect the amountof pedestrian traffic and/or types of streets/roads, and/or a level ofrisk associated with such.

The telematics and/or other data may indicate or include informationdetailing (i) an amount of vehicle traffic, and/or (ii) the types ofroads that the vehicle travels through or in on a daily or weekly basis.The travel environment generated may reflect the amount of vehicletraffic and/or types of roads, and/or a risk associated with such.

The telematics and/or other data may be collected over one or morevehicle trips or days, and is associated with multiple drivers of thevehicle. The telematics and/or other data may be used to identify thedriver driving the vehicle during each trip or day.

In one aspect, a computer system configured to provide auto insurancediscounts may be provided. The computer system may include one or moreprocessors and/or transceivers. The one or more processors may beconfigured to: (1) receive, via a transceivers, such as via wirelesscommunication or data transmission, telematics and/or other data from avehicle processor/transceiver or a mobile device of an insured; (2)determine an average travel environment that the vehicle travels in, theaverage travel environment accounting for heavy or light pedestriantraffic and/or heavy or light vehicle traffic that the vehicle typicallytravels in; (3) use the average travel environment to build a riskaverse profile for the insured; (4) generate or update an auto insurancediscount for the insured based upon their risk averse profile; and/or(5) transmit, via the transceiver, the auto insurance discount to theinsured's vehicle processor or mobile device for display for theinsured's review and/or approval such that insurance discounts areprovided based upon a risk associated with the travel environment thatan insured vehicle or insured typically travels within. The computersystem may include additional, less, or alternate functionality,including that discussed elsewhere herein.

For instance, the telematics and/or other data may indicate or includeinformation detailing (i) an amount of pedestrian traffic, and/or (ii)the types of streets that the vehicle travels through on a daily orweekly basis, and the risk averse profile may reflect the amount ofpedestrian traffic and/or types of streets. Additionally oralternatively, the telematics and/or other data may indicate or includeinformation detailing (i) an amount of vehicle traffic, and/or (ii) thetypes of roads that the vehicle travels through or in on a daily orweekly basis, and the risk averse profile may reflect the amount ofvehicle traffic and/or types of roads.

The telematics and/or other data may be collected over one or morevehicle trips or days. Additionally or alternatively, the telematicsand/or other data may be associated with multiple drivers of thevehicle, and the telematics and/or other data may be used to identify amember of household driving the vehicle during each trip or day.

In another aspect, a computer system configured to provide autoinsurance discounts may be provided. The computer system may include oneor more processors and transceivers. The one or more processors may beconfigured to: (1) receive, via a transceiver, such as via wirelesscommunication or data transmission, telematics and/or other data from avehicle processor/transceiver or a mobile device of an insured, thetelematics and/or other data indicative of a travel environment of thevehicle; (2) determine a risk profile for the vehicle that reflects atravel environment that the vehicle travels in, the travel environmentaccounting for pedestrian traffic and/or vehicle traffic that thevehicle typically travels in; (3) generate or update an auto insurancediscount for the insured or the vehicle based upon the risk profile;and/or (4) transmit, via the transceiver, the auto insurance discount tothe insured's vehicle or mobile device for display for the insured'sreview and/or approval such that insurance discounts are provided basedupon a risk associated with the travel environment that an insuredvehicle or insured typically travels within. The computer system mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer system configured to provide autoinsurance discounts may be provided. The computer system may include oneor more processors and transceivers. The one or more processors mayinclude (1) receive, via a transceivers, such as via wirelesscommunication or data transmission, telematics and/or other data from avehicle controller or processor, or a mobile device of an insured; (2)generate or build a travel environment for the vehicle using or basedupon the telematics and/or other data, the travel environment accountingfor pedestrian traffic and/or vehicle traffic that the vehicle typicallytravels in or with; (3) generate or update an auto insurance discountfor the insured or the vehicle based upon the travel environment; and/or(4) transmit, via the transceivers, the auto insurance discount to theinsured's vehicle or mobile device for display for the insured's reviewand/or approval such that insurance discounts are provided based upon arisk associated with the travel environment that an insured vehicle orinsured typically travels within. The computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

FIG. 4 illustrates a flow diagram of an exemplary risk assessment method400 for determining risks associated with operation of a vehicle 108.The method 400 may begin with receiving telematics data associated withvehicle operation over a period of time (block 402). Based upon thisreceived telematics data, one or more typical travel environments may bedetermined (block 404). From such typical travel environments, one ormore risk levels associated with the vehicle 108 or one or more vehicleoperators of the vehicle 108 may be determined (block 406). The risklevels determined may be used to further determine a discount for aninsurance policy (block 408). The determined discount may then bepresented to the vehicle owner, vehicle operator, or insured party forreview or approval (block 410).

At block 402, the external computing device 206 may receive dataassociated with vehicle operation of the vehicle 108. The data mayinclude information regarding the operation of the vehicle (e.g., speed,acceleration, braking, etc.) as well as information regarding thevehicle operating environment in which the vehicle 108 operates. Thedata regarding the vehicle operating environment may include indicationsof the vehicle location and time of day associated with vehicleoperation. In further embodiments, the operating environment data mayalso include data regarding traffic conditions, weather conditions, roadconditions (e.g., construction, lane closures, etc.), or otherrisk-related conditions. Such other risk-related conditions may includelevel of pedestrian traffic, level of bicycle traffic, type of roadway,activity of wild animals on or near the roadway, or similar externalconditions that may affect the risk associated with operation of thevehicle 108. The data may be received directly or indirectly from thevehicle 108 (i.e., from a mobile computing device 110 or on-boardcomputer 114 disposed within or associated with the vehicle 108). Datamay also be received from other vehicles 202 operating within thevicinity of the vehicle 108, from sensors of smart infrastructurecomponents 208, or from databases or other sources based upon sensordata. For example, GPS location data obtained from a locationacquisition unit 320 of a mobile computing device 110 within the vehicle108 may be used to query weather data from the National Weather Serviceor other databases of weather data. In some embodiments, the externalcomputing device 206 may be one or more servers operated by or on behalfof an insurer or third party risk assessor to process data regardingvehicle usage.

At block 404, the external computing device 206 may use the receiveddata to determine one or more travel environments in which the vehicle108 operates. This may include determining one or more typical,frequent, or average travel environments based upon the received data.Travel environments may include data regarding aspects of the operatingenvironment through which the vehicle 108 travels that affect theprobability of a vehicle accident or other loss event. Such aspects ofthe operating environment may include time of day, location (e.g., city,suburban, rural, etc.), type of road (e.g., residential streets,restricted access highway, etc.), traffic levels, pedestrian trafficlevels, or other similar aspects. Even in embodiments in which the datais received by the external computing device 206 on a continuous basis(e.g., during vehicle operation or after each vehicle trip), a travelenvironment may be determined based upon data covering a period ofoperation (e.g., a week, a month, ten vehicle trips, etc.). The one ormore travel environments may include the usual operating environment forthe vehicle 108, such as an environment associated with daily commuting.Where more than one travel environment is determined for the vehicle108, each travel environment may be associated with a proportion oftotal vehicle operation spent in the travel environment. In someembodiments, the external computing device 206 may determine a travelenvironment profile for the vehicle 108, indicating the one or moretravel environments determined to be associated with the vehicle 108.Where multiple drivers use the same vehicle, travel environments may bedetermined for total vehicle usage or may be determined separately foreach driver.

At block 406, the external computing device 206 may determine risksassociated with the vehicle 108 and/or a vehicle operator associatedwith the vehicle 108 based upon the determined one or more travelenvironments. The risks may be associated with vehicle operation in eachof multiple travel environments or may be associated with a total riskacross all travel environments. The risks may be indicative of levels ofrisk associated with a particular vehicle operator of the vehicle 108,which may be expressed as scores, probabilities, categories, or othermetrics. In some embodiments, the risks may be determined as a riskprofile or a risk averse profile, as discussed above. A risk profile mayinclude information regarding the risks associated with operation of thevehicle 108 in the determined one or more travel environments, which mayinclude risks associated with traffic levels, types of roadways,pedestrian traffic levels, etc. A risk averse profile may be includeinformation regarding the risks associated with a particular vehicleoperator based upon the determined one or more travel environments,which may include risks associated with time of travel, traffic levels,types of roadways, location of vehicle operation, etc. In someembodiments, multiple risk profile or risk averse profiles may bedetermined for different combinations of vehicle and drivers.

At block 408, the external computing device 206 may determine a discountfor an insurance policy associated with the vehicle 108 based upon thedetermined risks. The discount may be associated with the vehicle 108 ormay be associated with a particular vehicle operator of the vehicle 108.The discount may be determined based upon a comparison of the risks forthe vehicle 108 or vehicle operator with usual risk levels for similarlysituated vehicles or vehicle operators. For example, a discount may bedetermined because the vehicle 108 is primarily driven in low-risktravel environments (e.g., daylight hour driving on low-traffic roadswith little pedestrian traffic, etc.). A level of the discount may bedetermined based upon the difference between usual risk levels forsimilarly situated vehicles and the risk levels determined based uponthe received data. Although this determination is described as adiscount, it may similarly take the form of an incentive program,rewards points, a reduction in deductible, a change in premium, a changein coverage, or similar changes to an insurance policy.

At block 410, the external computing device 206 may cause the discountto be presented to the vehicle owner, vehicle operator, insured party,or other interested person or organization. The discount may bepresented via a mobile computing device 110, on-board computer 114, orother external computing device 206 (e.g., a home computer, tablet,laptop, etc.). In some embodiments, the discount may be presented forreview and/or approval prior to being implemented. In furtherembodiments, the discount may be implemented by applying the discount tothe insurance policy. In appropriate cases, the external computingdevice 206 may facilitate appropriate funds transfers between an insurerand an insured related to the discount.

Accident Cause Determination/Accident Reconstruction

In one aspect, a computer-implemented method of accident causedetermination and/or accident reconstruction may be provided. The methodmay include (1) receiving, via one or more processors (or associatedtransceivers), such as via wireless communication or data transmission,smart traffic light data from a smart traffic light transceiver, thesmart traffic light data including time-stamped data associated withtimes when the traffic light was red, green, and yellow before, during,and/or after a vehicle accident; (2) receiving, via the one or moreprocessors (or associated transceivers), such as via wirelesscommunication or data transmission, vehicle or mobile devicetime-stamped GPS (Global Positioning System) and/or speed data (and/orother telematics data) from a vehicle or mobile device transceiveracquired before, during, and/or after the vehicle accident; (3)comparing, via the one or more processors, the time-stamped smarttraffic light data with the time-stamped GPS and/or speed data (and/orother telematics data) to determine if the vehicle or another vehiclewas a cause of the vehicle accident occurring at an intersectionassociated with the smart traffic light; and/or (4) updating, via theone or more processors, an insurance policy premium or discount basedupon which vehicle caused the vehicle accident to facilitate notpenalizing not-at-fault drivers and/or generating insurance premiums ordiscounts more reflective of actual risk, or lack thereof, associatedwith certain types of vehicles and/or risk averse drivers. The one ormore processors may include processors of one or more external computingdevices 206, such as servers associated with an insurer, investigator,or law enforcement agency. The method may include additional, less, oralternate actions, including those discussed herein.

For instance, the method may include generating, via the one or moreprocessors, a virtual reconstruction of the vehicle accident whichincludes a graphical representation of the traffic light changing. Oneor more vehicles involved in the vehicle accident may be autonomous orsemi-autonomous vehicles.

In another aspect, a computer-implemented method of accident causedetermination and/or accident reconstruction may be provided. The methodmay include (1) receiving, via one or more processors (or associatedtransceivers), such as via wireless communication or data transmission,smart traffic light data from a smart traffic light transceiver, thesmart traffic light data including time-stamped data associated withtimes when the traffic light was red, green, and yellow (before, during,and/or after a vehicle accident); (2) receiving, via the one or moreprocessors (or associated transceivers), such as via wirelesscommunication or data transmission, vehicle or mobile devicetime-stamped GPS (Global Positioning System) and speed data (and/orother telematics data) from a vehicle or mobile device transceiver(acquired before, during, and/or after a vehicle accident); (3)comparing, via the one or more processors, the time-stamped smarttraffic light data with the time-stamped GPS and speed data to (i)determine if the vehicle was traveling in accordance with the color ofthe smart traffic light at a time that a vehicle accident occurred at anintersection associated with the smart traffic light, or (ii) otherwisedetermine that the vehicle or driver (insured) did not cause the vehicleaccident; and/or (4) updating, via the one or more processors, aninsurance policy premium or discount based upon the vehicle or drivernot causing the vehicle accident to facilitate not penalizingnot-at-fault drivers and/or generating insurance premiums or discountsmore reflective of actual risk, or lack thereof, associated with certaintypes of vehicles and/or risk averse drivers. Again, the one or moreprocessors may include processors of one or more external computingdevices 206, such as servers associated with an insurer, investigator,or law enforcement agency. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In one aspect, a computer system configured to perform accidentreconstruction may be provided. The computer system may include one ormore processors configured to: (1) receive, via a transceiver, such asvia wireless communication or data transmission, smart traffic lightdata from a smart traffic light transceiver, the smart traffic lightdata including time-stamped data associated with times when the trafficlight was red, green, and yellow before, during, and/or after a vehicleaccident; (2) receive, via the transceiver, such as via wirelesscommunication or data transmission, vehicle or mobile devicetime-stamped GPS (Global Positioning System), speed, braking, and/oracceleration data (acquired before, during, and/or after the vehicleaccident) from a vehicle or mobile device transceiver; (3) compare thetime-stamped smart traffic light data with the time-stamped GPS andspeed data to determine if the vehicle or another vehicle was a cause ofthe vehicle accident occurring at an intersection associated with thesmart traffic light; and/or (4) update an insurance policy premium ordiscount based upon which vehicle caused the vehicle accident tofacilitate not penalizing not-at-fault drivers and/or generatinginsurance premiums or discounts more reflective of actual risk, or lackthereof, associated with certain types of vehicles and/or risk aversedrivers. Again, the one or more processors may include processors of oneor more external computing devices 206, such as servers associated withan insurer, investigator, or law enforcement agency. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

For instance, the one or more processors may be further configured togenerate a time-lapsed virtual reconstruction of the vehicle accidentwhich includes a graphical representation of the traffic light changing,and the speed and location of the vehicle with respect to theintersection. The one or more processors may be configured to transmit,via the transceiver, the updated auto insurance discount to the insuredfor their review and/or approval. The one or more vehicles involved inthe vehicle accident may be autonomous or semi-autonomous vehicles.

In another aspect, a computer system configured to perform accidentreconstruction may be provided. The computer system may include one ormore processors, which may include processors of one or more externalcomputing devices 206, such as servers associated with an insurer,investigator, or law enforcement agency. The one or more processors maybe configured to: (1) receive, via a transceiver, such as via wirelesscommunication or data transmission, smart traffic light data from asmart traffic light transceiver, the smart traffic light data includingtime-stamped data associated with times when the traffic light was red,green, and yellow (and acquired or generated before, during, or after avehicle accident); (2) receive, via the transceiver, such as viawireless communication or data transmission, vehicle or mobile devicetime-stamped GPS (Global Positioning System) and speed data (and/orother telematics data) from a vehicle or mobile device transceiver; (3)compare the time-stamped smart traffic light data with the time-stampedGPS and speed data (and/or other telematics data, such as accelerationor braking data) to (i) determine if the vehicle was traveling inaccordance with the color of the smart traffic light at a time that thevehicle accident occurred at an intersection associated with the smarttraffic light, or (ii) otherwise determine that the vehicle or driver(insured) did not cause the vehicle accident; and/or (4) update aninsurance policy premium or discount based upon the vehicle or drivernot causing the vehicle accident to facilitate not penalizingnot-at-fault drivers and/or generating insurance premiums or discountsmore reflective of actual risk, or lack thereof, associated with certaintypes of vehicles and/or risk averse drivers. The computer system mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

FIG. 5 illustrates a flow diagram of an exemplary accident analysismethod 500 for determining the cause of an accident or reconstructing anaccident using data from a smart infrastructure component 206. Themethod 500 may begin by receiving time-stamped data from one or moresmart infrastructure components (block 502) and time-stamped dataregarding one or more vehicles (block 504). From this received data, anaccident cause may be determined by comparison of the data from theinfrastructure components and vehicles (block 506). In some embodiments,determining the cause of an accident may include reconstructing someaspects of the accident, such as the position of one or more vehicles orthe state of an infrastructure component (e.g., a smart traffic signal).Based upon the determination of the cause of the accident, an update oradjustment to an insurance policy may be determined (such as a discountfor a driver not a fault) (block 508). Although the method is describedas separately receiving the infrastructure component data and thevehicle data, it should be understood that these may be receivedtogether or separately in any order in various embodiments. The data maybe received by one or more external computing devices 206, such asservers associated with an insurer, investigator, or law enforcementagency.

At block 502, an external computing device 206 may receive time-stampeddata from one or more infrastructure components 208 (or sensors attachedto or disposed within infrastructure components 208). Such data may bereceived in response to a request for the data, such as during theinsurance claims adjusting process following an accident. Such requestmay be made in near real-time as an anomalous event (or potentialanomalous event) occurs, or may be made at a later time to a serverstoring recorded data associated with one or more infrastructurecomponents 208. Alternatively, the infrastructure component 208 maydetermine that an anomalous event has occurred, such as an accident ornear miss between vehicles, and transmit the time-stamped data to theexternal computing device 206. The time-stamped infrastructure componentdata may include sensor data collected by the infrastructure component208 or data regarding the state of the infrastructure component 208. Forexample, the data may indicate times when a traffic signal changedbetween various states (e.g., when a traffic light changed betweengreen, yellow, and red, when a railroad crossing signal sounded orlowered a gate, etc.). Sensor data may include sensed data regardingweather or traffic conditions, such as temperature, precipitation(falling or accumulated), road icing (e.g., accumulation of ice,presence of conditions conducive of icing, time since last salting orplowing, etc.), wind speed, construction work, lane closures, accidents,traffic flow (e.g., vehicles per minute, average vehicle speed, vehiclespacing, etc.), or similar data.

At block 504, the external computing device 206 may receive time-stampeddata from one or more vehicles 108 (202.1-202.N). The vehicle data mayinclude telematics data collected by, generated by, or received fromsensors by a mobile computing device 110 and/or on-board computer 114 ofeach vehicle 108. Such vehicle data may include data regarding theoperation, path, or movement of vehicles. For example, the vehicle datamay indicate a series of locations of one or more vehicles 108 during arelevant time period (e.g., a time period including a vehicle accident).In some embodiments, such vehicle data may include location data (e.g.,GPS location data, other geocoordinate data, or relative location dataindicating position relative to other vehicles or infrastructurecomponents), velocity data, acceleration data (e.g., increasing ordecreasing speed), and/or operation data (e.g., use of signals,application of brakes, throttle position, use of driver assistancefunctionalities of the vehicle, etc.). The vehicle data may be receivedonly from the vehicle or vehicles of interest (e.g., vehicles involvedin an accident). Alternatively, or additionally, vehicle data may bereceived from other vehicles 202 in the vicinity of the vehicles ofinterest. Such other vehicle data may provide important informationregarding traffic flow, road conditions, or movement of the vehicles ofinterest. The other vehicle data may be indicative of the othervehicles, environmental conditions, or vehicles of interest. In suchmanner, data regarding movement or operation of vehicles of interest maybe obtained even for vehicles of interest that lack telematics datagathering capabilities (i.e., vehicles 108 without any mobile computingdevices 110, on-board computers 114, or similar components to collect orrecord vehicle telematics data).

At block 506, the external computing device 206 may determine a cause ofan accident, such as a collision. In some embodiments, such accidentsmay include a collision between vehicles, a collision between a vehicleand a pedestrian, a collision between a vehicle and infrastructure, acollision between a vehicle and animals, or a collision between avehicle and another object (e.g., debris in a roadway, a mailbox, atree, etc.). In further embodiments, such accidents may encompass nearmisses that do not result in collisions or other high-risk events (e.g.,vehicle spin-outs, fishtailing, sliding off a roadway, etc.), regardlessof whether any collision or damage occurred. Determination of the cause(or causes) of an accident may be performed by comparison of thetime-stamped data from infrastructure components 208 and/or vehicles108. As an example, time-stamped location and signal status dataassociated with a time period including a collision between two vehiclesin an intersection may indicate that one of the two vehicles involved inthe collision entered the intersection at an improper time (i.e.,against a red light), thereby causing the accident. As another example,time-stamped data may indicate that a vehicle 108 lost traction on aroadway with conditions conducive to the formation of ice, but that thevehicle 108 engaged in hard braking at the time of loss of traction. Insome embodiments, the external computing device 206 may generate avirtual reconstruction of relevant portions of the operating environmentin which an accident occurred. For example, the external computingdevice 206 may generate a virtual reconstruction of the locations of oneor more vehicles 202 or other objects within the vehicle operatingenvironment at or around the time of an accident based upon datareceived from infrastructure components 208 and vehicles 202. In someembodiments, a static or dynamic graphical representation of the vehicleoperating environment may be generated for presentation to a user of theexternal computing device 206 by comparison of the received data. Suchgraphical representation may be a 2-D, 3-D, or 4-D (moving 3-D)reconstruction of the accident, which may further include graphicalindicators (e.g., color coding, icons, etc.) of key events or conditions(e.g., signal changes, vehicle braking/acceleration, vehicle right ofway, etc.). Such reconstructions may be presented to a user fordetermination or verification of fault for accidents.

At block 508, the external computing device 206 may determine an updateto an insurance policy associated with a vehicle 108 involved in theaccident based upon the determination of the cause of the accident. Theupdate may include a change to a premium, a coverage level, a coveragetype, an exclusion, an insured driver, or other aspects of the policy.Some updates may include not changing any aspects of the insurancepolicy, such as where the accident is determined to be caused by anothervehicle. For example, a discount associated with a safe driving recordmay be maintained (or reinstated) upon determination that the accidentwas caused by another vehicle. As another example, a discount may beapplied to the insurance policy associated with a driver of a vehicle108 determined not to be at fault for the accident, which discount mayoffset an effect on the insurance policy arising from the accident. Inother instances, the update may include an indirect change to theinsurance policy through a change to a risk assessment or risk profileassociated with the vehicle 108 (or a driver of the vehicle 108). Theupdate may be presented to a vehicle owner or an insured party forreview and/or approval in some embodiments. In other embodiments, theupdate may be implemented to adjust or maintain the insurance policyterms, which may include facilitating a payment, money transfer, orbilling event. The method 500 may then terminate.

In some aspects, data from one or more vehicles 202.1-202.N,infrastructure components 208, and/or other sources may be collected todetermine the occurrence and causes of anomalous conditions. Suchanomalous conditions may include accidents, near-misses, environmentalconditions (e.g., traffic back-ups, potholes, flooding, etc.), orsimilar conditions that occur in a vehicle operation environment. Suchdata may be continuously monitored to determine the occurrence ofanomalous conditions, or it may be stored to facilitate identificationof anomalous conditions at a later time, if needed.

FIG. 6 illustrates a flow diagram of an exemplary anomalous conditionmonitoring method 600 for monitoring the environment in which a vehicle108 is operating (or located) and determining the occurrence of ananomalous condition in the vehicle environment. The method 600 may beginwith monitoring the vehicle environment using sensor data from one ormore sensors within the vehicle or vehicle environment (block 602).Based upon the sensor data, the occurrence of an anomalous condition maybe determined (block 604). Upon such determination, relevant sensor datamay be recorded to facilitate analysis of the anomalous condition (block606). In some embodiments, part or all of the recorded data may betransmitted to another computing device for further analysis (block608). In some embodiments, the vehicle environment may be monitored foranomalous conditions only while the vehicle is in operation, while inother embodiments the vehicle environment may be monitored even when thevehicle is parked or otherwise at rest. Monitoring while the vehicle isparked may be of particular use in theft prevention and recovery.

At block 602, the vehicle environment associated with the vehicle 108may be monitored by one or more sensors disposed within the vehicle 108.Sensor data from other vehicles 202 and/or infrastructure components 208may also be monitored. The sensor data may be communicated to a mobilecomputing device 110 or on-board computer 114 within the vehicle 108 orto an external computing device 206 for analysis, via the network 201 ordirect communication links. For example, transceivers of vehicles withinthe vehicle environment may communicate sensor data or other datadirectly between vehicles 202, which may include distance betweenvehicles. The sensor data may indicate vehicle information, such as thelocation, movement, or path of the vehicle 108 or other vehicles 202within the vehicle environment. The sensor data may similarly includeenvironmental information, such as the weather, traffic, construction,pedestrian, or similar conditions within the vehicle environment. Suchsensor data may be stored in a memory associated with the vehicle 108(such as a mobile computing device 110 or on-board computer 114) orassociated with one or more external computing devices 206.

At block 604, the mobile computing device 110, on-board computer 114, orexternal computing device 206 may determine the occurrence of ananomalous condition based upon the received sensor data. Such anomalousconditions may include accidents, weather conditions, trafficconditions, construction or other roadway condition, and/or high-riskconditions. High-risk conditions may include transient conditions (e.g.,reckless driving, a vehicle swerving between lanes, blinding sun at dawnor dusk, heavy pedestrian traffic, etc.) or non-transient conditions(e.g., confusing or otherwise high-risk intersections, blind corners,winding down-slope road segments, etc.). When determined by the mobilecomputing device 110 or on-board computer 114, the anomalous conditionmay be related to the immediate vehicle environment (e.g., accidents,reckless driving, impaired driving, vehicle emergencies, vehiclebreakdowns, potholes, lane closures, etc.) in some embodiments. Infurther embodiments, the external computing device 206 may alsodetermine anomalous conditions affecting many drivers (e.g., trafficjams, heavy pedestrian traffic such as in the vicinity of a sportingevent, etc.). Determining the occurrence of an anomalous condition mayinclude comparing sensor data with previously recorded data for thelocal environment (e.g., based upon GPS data) or for similarenvironments (e.g., residential streets within a city, rural highways,etc.). Additionally, or alternatively, determining the occurrence of ananomalous condition may include determining the occurrence of ananomalous condition indicator based upon the sensor data (e.g., distancebetween vehicles falling below a threshold, misalignment between vehicleorientation and path, rapid acceleration or braking, speed less than athreshold amount above or below a posted speed limit, etc.).

At block 606, sensor data related to the anomalous condition may berecorded for further analysis. This may include recording sensor datafor a period of time beginning before the determination of theoccurrence of the anomalous condition at block 604 (such as by movingdata from a buffer or volatile memory to a permanent storage ornon-volatile memory) and extending for a period of time afterdetermination of the anomalous condition. Sensor data may be storedlocally within the vehicle 108, such as in data storage 360 associatedwith the mobile computing device 110 or on-board computer 114, or thesensor data may be stored in a memory or database associated with theexternal computing device 206. Alternatively, recording the sensor datamay involve maintaining the sensor data only for a short period of timeto transmit the data to an external computing device 206. In someembodiments, recording sensor data related to the anomalous conditionmay involve activating one or more additional sensors, as discussedbelow.

At block 608, the recorded sensor data may be transmitted to anothercomputing device for further analysis. This may include transmitting therecorded sensor data from the vehicle 108 to the external computingdevice 206 via the network 201. Such transmission may begin immediatelyupon determination of the occurrence of the anomalous condition, ortransmission may be delayed until some later time. For example, thetransmission may be delayed until the vehicle 108 is garaged or themobile computing device 110 or on-board computer 114 is communicativelyconnected to a WiFi network. Alternatively, the recorded data may bedirectly collected from a local storage device disposed within thevehicle 108, such as a crash-resistant storage (e.g., a “black box”storage device).

In particularly advantageous embodiments, the sensor data may be used tomonitor the operation of other vehicles 202 in the vicinity of thevehicle 108. In such embodiments, the mobile computing device 110 oron-board computer 114 may process the data in real-time as it isreceived to determine whether an anomalous condition has occurred basedupon the movement of other vehicles 202. For example, the sensor datamay indicate that a vehicle 202.1 is engaged in hard braking (indicatedby a rapid decrease in speed), which may have been caused by a suddenlane change of a vehicle 202.2 ahead of vehicle 202.1. The hard brakingmay trigger the vehicle 108 to record data associated with the incident,which may be transmitted to an external computing device 206 for furtheranalysis. Such further analysis may indicate that the vehicle 202.2caused the anomalous condition by reckless vehicle operation. Thisinformation may be further used to appropriately adjust risk levels orinsurance policies (e.g., premiums, discounts, coverage levels, etc.)associated with the vehicles 202. For example, the sensor data recordedby vehicle 108 may include still or video images indicating the lanechange of vehicle 202.2 caused an accident involving vehicle 202.1 andanother vehicle 202.3, without involving vehicle 202.1. Such sensor datamay be used for legal proceedings, claims adjustment, and/or rateadjustment to ensure a proper assignment of fault, as discussed below.

FIG. 7 illustrates a flow diagram of an exemplary anomalous conditionanalysis method 700 for analyzing the types and causes of anomalousconditions. The method 700 may begin by receiving data associated withan anomalous condition recorded in accordance with method 600 above orotherwise (block 702). The received data may then be used to reconstructthe path of a vehicle within the vehicle operating environment (block704), from which it may be determined whether the vehicle analyzedcaused the anomalous condition (block 706). An update to an insurancepolicy associated with the analyzed vehicle may then be determinedand/or implemented based upon the determination of whether the vehicleanalyzed caused the anomalous condition (block 708). The method 700 maybe repeated for any number of vehicles within a vehicle operatingenvironment based upon the received data. In some embodiments, multiplevehicles may be determined to be partially at fault for causing ananomalous condition, in which case fault may be apportioned between thevehicles.

At block 702, the external computing device 206 may receive dataassociated with an anomalous condition relating to a vehicle 108. Thedata may be received from sensors within the vehicle 108 and/or withinother vehicles 202 in proximity to the vehicle 108. Data may also bereceived from sensors of smart infrastructure components 206. The datamay indicate the environmental conditions in which the vehicle 108 wasoperating, the location and movement of the vehicle 108, and/or thelocations and movements of other vehicles 202. Such data may include GPScoordinates, as well as operating data from the vehicle 108 such asindications of speed and acceleration. Although the data may relate tothe vehicle 108, it may also be associated with other vehicles 202within the operating environment of the vehicle 108, such as dataindicating the positions of the other vehicles 202. Such data may berelevant to vehicle 108 by indicating the relative positions andmovements of the vehicle 108 with respect to the other vehicles 202.

At block 704, the external computing device 206 may reconstruct themovements or path of the vehicle 108 based upon the received sensordata. This may include interpolation of the sensor data for timesbetween sensor data points. The may also include estimation of thelocation or properties of the vehicle 108 at one or more points of timebased upon sensor data from a plurality of other vehicles 202 (such asby triangulation). In some embodiments, properties of the vehicle 108(e.g., whether the vehicle 108 signaled a turn, whether the vehicle 108had illuminated its headlights, etc.) may be determined by analysis ofthe sensor data from the vehicle 108 and/or other vehicles 202 (such asby object detection in images of the vehicle 108). The reconstructedmovements or path of the vehicle 108 may be associated with one or morereference times (e.g., time-stamps) to facilitate determination of thecause of the anomalous condition. Also to facilitate such analysis, insome embodiments, the movements or paths of other vehicles 202 and/orother aspects of the vehicle operating environment may be reconstructedfor a period including the anomalous condition.

At block 706, the external computing device 206 may determine whetherthe vehicle 108 caused the anomalous condition based upon the receivedsensor data. This may involve comparing the reconstructed path of thevehicle 108 with the paths of the other vehicle 202 in the vehicleoperating environment and/or other environmental conditions (e.g., thestate of a traffic light at relevant times, the presence and location ofa wild animal on the roadway, the movement of other vehicles 202, etc.).In some embodiments, the vehicle 108 may be determined to have causedthe anomalous condition, even where the vehicle 108 is not directlyaffected by the anomalous condition. For example, one or more othervehicles 202 within the vehicle operating environment may collide in avehicle accident, but the vehicle 108 may not collide with any othervehicle 202 or other object within the vehicle operating environment.The movements of the vehicle 108 (e.g., changing lanes without using asignal, swerving between lanes, cutting off one of the vehicle 202,etc.), however, may nonetheless be determined to be the cause of thevehicle accident. If the vehicle 108 is determined to be the cause ofthe anomalous condition, a proportion of the fault for the anomalouscondition may be further determined for the vehicle 108.

At block 708, the external computing device 206 may determine an updateto an insurance policy associated with a vehicle 108. If the vehicle 108is determined to have caused the anomalous condition, the adjustment mayinclude a change to a premium, a coverage level, a coverage type, anexclusion, an insured driver, or other aspects of the policy to reflectthe risk associated with the operation of the vehicle 108. This mayinclude a determination of a risk level or severity of the anomalouscondition. For example, a multi-vehicle accident caused by the vehicle108 may lead to the determination of a greater increase in a premiumthan would be determined for a near miss caused by the vehicle 108stopping abruptly. Some updates may include not changing any aspects ofthe insurance policy, such as where the vehicle 108 has been determinednot to have caused the anomalous condition. In some embodiments, theupdate may be presented and/or implemented, as discussed elsewhereherein.

Electric Vehicle Battery Conservation

In one aspect, a computer-implemented method of accident causedetermination and/or accident reconstruction for an electric orbattery-powered vehicle may be provided. The method may include (1)receiving, via one or more processors (such as via a smart vehiclecontroller), an indication of a trigger event; (2) turning on, via theone or more processors, a front facing camera or video camera mounted onthe vehicle, the front facing camera or video camera configured toacquire or take images in front of, or to the side of, a moving vehicle;and/or (3) transmitting, via the one or more processors (or anassociated transceiver), the image data associated with the imagesacquired after the trigger event is detected to a remote server forcomputer analysis of the image data to facilitate not only accidentreconstruction, but to also facilitate conserving a battery powering anelectric vehicle and only turning on a video camera immediately prior toan anticipated or actual vehicle collision. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

For instance, the trigger event may be any one or more of the following:the one or more processors detecting vehicle speed unexpectedly orrapidly decreasing; the one or more processors detecting the vehiclefollowing distance unexpectedly or rapidly decreasing; the one or moreprocessors detecting a brake pedal being engaged or otherwise triggeredby brake system pressure; and/or the one or more processors detecting ananimal in the vicinity of the vehicle, such as via an infrared cameradetecting a deer after sunset. Other trigger events may be used.

In another aspect, a computer system configured to perform accidentreconstruction for an electric or battery-powered vehicle may beprovided. The computer system may include one or more processors. Theone or more processors may be configured to: (1) receive or determine anindication of a trigger event, such as via computer analysis oftelematics and/or other data gathered by one or more sensors; (2) turnon a front facing camera or video camera (or other type of datarecording system) mounted on the vehicle, the front facing camera orvideo camera configured to acquire or take images in front of, or to theside of, a moving vehicle; and/or (2) transmit, via a transceiver, theimage data associated with the images acquired after the trigger eventis detected to a remote server for computer analysis of the image datato facilitate not only accident reconstruction, but to also facilitateconserving a battery powering an electric vehicle and only turning on avideo camera immediately prior to an anticipated or actual vehiclecollision. The computer system may include additional, less, oralternate functionality, including that discussed elsewhere herein, andthe trigger events may be those discussed above.

Although the methods described herein may be of particular value whenused with electric vehicles due to the direct battery drain, the methodsmay also be implemented to save fuel or power consumed by other vehicles(e.g., gasoline- or diesel-powered vehicles, hybrid vehicles, naturalgas-powered vehicles, etc.). Similarly, sensors other than front facingcameras or video cameras may be activated when triggered, which maylikewise reduce power consumption while recording particularly importantdata during vehicle operation. Such other sensors may be used inaddition to, or as alternatives to, front facing cameras or videocameras.

In some embodiments, one or more sensors may continually monitor vehicleoperation. Such continuously monitoring sensors may be supplemented byadditional sensors (such as front facing cameras or video cameras) uponthe occurrence of a trigger event. In some embodiments, such sensors maystore data locally within a program memory or storage medium within thevehicle 108 until the occurrence of a trigger event, at which point someor all of the recorded data may be transmitted via the network 201 to anexternal computing device 206 for further storage and/or analysis. Forexample, speedometer data may be continuously recorded, but a recentperiod (e.g., ten seconds, thirty seconds, five minutes, etc.) may betransmitted upon an indication of hard braking. Such data may beanalyzed to determine whether the vehicle 108 was operated in anaggressive manner preceding the hard braking event (which may beassociated with a heightened risk of a vehicle accident). In someembodiments, such determination may be used to assign fault for theaccident and/or adjust an insurance policy.

FIG. 8 illustrates a flow diagram of an exemplary battery conservationmethod 800 for reducing drain on a power source while recording datarelevant to accident reconstruction. The battery conservation method 800may begin by monitoring the operation of a vehicle 108 by one or moresensors (block 802) until an indication of a trigger event is received(block 804). Upon receiving the indication of the trigger event, one ormore additional sensors within the vehicle 108 may be activated (block806) to record additional data associated with the vehicle operatingenvironment (block 808). In some embodiments, the additional datarecorded by the one or more additional sensors may be transmitted to anexternal computing device 206 for further analysis (block 810). When itis determined that the condition associated with the trigger event hasbeen sufficiently recorded (block 812), the one or more additionalsensors within the vehicle 108 are deactivated to conserver power (block814).

At block 802, the method 800 may begin with monitoring the operation ofthe vehicle 108 or the environment in which the vehicle 108 is operating(such as other vehicles 202.1-202.N). This may include monitoring thespeed, acceleration, braking, trajectory, or location of the vehicle 108using a mobile computing device 110 or on-board computer 114. This mayalso include receiving data from sensors disposed within other vehicles202 or infrastructure components 208. The data may include telematicsdata collected or received by a Telematics App, as discussed elsewhereherein. In some embodiments, the operation of the vehicle 108 may bemonitored using only those sensors that are also used for vehiclecontrol, navigation, or driver alerts (e.g., adaptive cruise control,autonomous piloting, GPS location, or lane deviation warnings). Infurther embodiments, an external computing device 106 may monitor thevehicle 108 and is operating environment using data from sensors withinthe vehicle or environment. Because of the delay caused by suchcommunications, however, a mobile computing device 110 or on-boardcomputer 114 may be used for such monitoring.

At block 804, the mobile computing device 110 or on-board computer 114receives an indication of a trigger event. The trigger event may bebased upon the data from one or more sensors used to monitor the vehicleand its environment, as described above. The indication of the triggerevent may be generated by the mobile computing device 110 or on-boardcomputer 114 based upon the sensor data or may be received from anotherdevice within the system 200. For example, another vehicle 202 maygenerate and transmit the indication of the trigger event to the vehicle108 directly or via the network 201. As another example, the externalcomputing device 206 may generate and transmit the indication to thevehicle 108 via the network 201 based upon sensor data (e.g., GPSlocation data indicating the vehicle 108 is approaching a dangerousintersection or traffic back-up). As noted above, trigger events mayinclude sudden changes in vehicle speed (either the vehicle 108 oranother nearby vehicle 202), which may be caused by sharp turning,swerving, hard braking, or hard acceleration. Distances between thevehicle 108 and other objects (e.g., other vehicles 202, infrastructurecomponents 208, pedestrians, animals on a roadway, etc.) may also beused as trigger event, either directly (e.g., a threshold distance toanother object, such as one meter, three meters, etc.) or indirectly(e.g., a rate of change in distance to another object, such as adecrease in distance of more than one meter per second, etc.). Thepresence of specified conditions may further be used as a trigger event,such as road construction, an animal identified in the roadway (such asby infrared sensors), or rush hour driving (e.g., 4-6 p.m. onnon-holiday weekdays). Some embodiments may use more or less inclusivemetrics or thresholds to generate an indication of a trigger event. Forexample, each depression of a brake pedal may be a trigger event,including ordinary braking in the ordinary course of vehicle operation.

At block 806, one or more additional sensors within the vehicle 108 areactivated based upon the received indication of the trigger event. Suchadditional sensors may use particularly high levels of power or may havelimited storage capacity. For example, high-quality digital cameras orhigh-speed video cameras may be activated, which may use significantpower and produce large amounts of data. Alternatively, the additionalsensors may be similar in type or power consumption to other sensorsused within the vehicle 108, but the one or more additional sensors mayprovide additional data to allow reconstruction of an accident that mayoccur. In some embodiments, the additional sensors may be disposed torecord images, sounds, or other information from the interior of thevehicle.

At block 808, the one or more additional sensors may record dataregarding the movement or operation of the vehicle 108 or regarding thevehicle operating environment in which the vehicle 108 is operating.Such data may be stored locally in a memory of a mobile computing device110 or on-board computer 114. In some embodiments, the recorded data maybe transmitted via the network 201 to the external computing device 206for storage or analysis. In yet further embodiments, recorded data maybe stored locally within the mobile computing device 110 or on-boardcomputer 114 until such device is communicatively connected to ahigh-bandwidth network connection or a power source.

At block 812, the mobile computing device 110 or on-board computer 114may determine that the conditions related to the trigger event have beensufficiently recorded. This may be based upon the occurrence of anothercondition (e.g., the vehicle 108 coming to a rest, reduction inacceleration below a threshold, leaving an area having road constructionor high population density, etc.) or based upon passage of time (e.g., apreset period of thirty seconds after activation, etc.). The amount ofrecording determined to be sufficient may depend upon the type of datarecorded, as well as the type of event triggering the recording. Once ithas been determined that the condition has been sufficiently recorded,the one or more additional sensors may be deactivated or returned to astandby mode to conserver power within a battery or other power sourceof the vehicle 108 at block 814.

The method 800 may then terminate or restart at block 802. In someembodiments, the method 800 may operate continuously while the vehicle108 is running or in operation. In alternatively embodiments, the method800 may operate to monitor the vehicle's environment when the vehicle isnot operating. In such embodiments, power conservation may be importantto ensure sufficient power remains to start the vehicle.

Generating Vehicle-Usage Profile to Provide Discounts

In one aspect, a computer-implemented method of generating autoinsurance discounts may be provided. The method may include (1)detecting or determining, via one or more processors, which individualwithin a household is driving a vehicle, such as by analyzing data fromone or more sensors; (2) collecting, via the one or more processors,telematics data for that individual indicating their driving behaviorfor the vehicle for a single trip; (3) using the telematics datacollected to update or build, via the one or more processors, avehicle-usage profile for the vehicle, the vehicle-usage profileindicating how much and what time of day each member of a householdtypically drives or uses the vehicle, and their driving behavior whiledriving the vehicle; and/or (4) updating, via the one or moreprocessors, an auto insurance premium or discount for the household orthe vehicle based upon the vehicle-usage profile to provide insurancecost savings to lower risk households and/or risk averse drivers. Themethod may include transmitting, via the one or more processors (and/orassociated transceiver), the updated auto insurance discount to theinsured for their review and/or approval. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

In one aspect, a computer system configured to generate auto insurancediscounts may be provided. The computer system including one or moreprocessors and/or transceivers. The one or more processors may beconfigured to: (1) detect or determine which individual within ahousehold is driving a vehicle from analyzing data received or generatedfrom one or more sensors; (2) collect telematics data for thatindividual indicating their driving behavior for the vehicle for asingle trip; (3) use the telematics data collected to update or build avehicle-usage profile for the vehicle, the vehicle-usage profileindicating how much and what time of day each member of a householdtypically drives or uses the vehicle, and their driving behavior whiledriving the vehicle; and/or (4) update an auto insurance premium ordiscount for the household or the vehicle based upon the vehicle-usageprofile to provide insurance cost savings to lower risk householdsand/or risk averse drivers. The one or more processors may be configuredto transmit, via a transceiver, the updated auto insurance discount tothe insured or their mobile device for their review and/or approval. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In another aspect, a computer system configured to generate autoinsurance discounts may be provided. The computer system may include oneor more processors configured to: (1) receive, via a transceiver, suchas via wireless communication or data transmission, telematics data fora vehicle for one or more trips; (2) determine, for each trip, whichindividual within a household was driving the vehicle, and/or determinetheir driving behavior for each trip based upon the telematics data; (3)use the telematics data received (and/or the individual driver anddriving behavior determinations) to update or build a vehicle-usageprofile for the vehicle, the vehicle-usage profile indicating how muchand what time of day each member of a household typically drives or usesthe vehicle, and their driving behavior while driving the vehicle;and/or (4) update an auto insurance premium or discount for thehousehold or the vehicle based upon the vehicle-usage profile to provideinsurance cost savings to lower risk households. The one or moreprocessors may be configured to transmit, via a transceiver, the updatedauto insurance discount to the insured for their review and/or approval.The computer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In one aspect, a computer-implemented method of generating autoinsurance discounts may be provided. The method may include (1)receiving, via one or more processors (and/or an associatedtransceiver), such as via wireless communication or data transmission,telematics data for a vehicle for one or more trips; (2) determining,via the one or more processors, for each trip, which individual within ahousehold was driving the vehicle, and/or determining their drivingbehavior for each trip based upon the telematics data; (3) using, viathe one or more processors, the telematics data received (and/or theindividual driver and driving behavior determinations) to update orbuild a vehicle-usage profile for the vehicle, the vehicle-usage profileindicating how much and what time of day each member of a householdtypically drives or uses the vehicle, and their driving behavior whiledriving the vehicle; and/or (4) updating or generating, via the one ormore processors, an auto insurance premium or discount for the householdor the vehicle based upon the vehicle-usage profile to provide insurancecost savings to lower risk households. The one or more processors may beconfigured to transmit, via a transceiver, the updated auto insurancediscount to the insured for their review and/or approval. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

In another aspect, a computer-implemented method of generating autoinsurance discounts may be provided. The method may include (1)detecting or determining, via one or more processors, which individualwithin a household is driving a vehicle or sitting in the driver's seatat the outset of a vehicle trip, such as by analyzing data from one ormore sensors; (2) collecting, via the one or more processors, telematicsdata for that vehicle trip; (3) assigning (and storing) or associating,via the one or more processors, the telematics data for that vehicletrip to the individual within the household that was identified as thedriver during the vehicle trip; (4) determining a driving score for theindividual and/or vehicle trip based upon the one or more processorsanalyzing the telematics data for the vehicle trip; (5) updating orbuilding, via the one or more processors, a vehicle-usage profile forthe vehicle based upon the telematics data for the vehicle trip and/orthe driving score, the vehicle-usage profile indicating how much andwhat time of day each member of a household typically drives or uses thevehicle, and their driving behavior while driving the vehicle; and/or(6) updating, via the one or more processors, an auto insurance premiumor discount for the household or the vehicle based upon thevehicle-usage profile to provide insurance cost savings to lower riskhouseholds and/or risk averse drivers. The one or more processors may beconfigured to transmit, via a transceiver, the updated auto insurancediscount to the insured for their review and/or approval. The one ormore processors may be local to vehicle, such as mounted within a mobiledevice and/or mounted on or within the vehicle or a vehicle controller.Additionally or alternatively, the one or more processors may be remoteto the vehicle, such as a remote located server associated with aninsurance provider. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system configured to generate autoinsurance discounts may be provided. The computer system may include oneor more processors or transceivers. The one or more processors may beconfigured to: (1) detect or determine which individual within ahousehold is driving a vehicle or sitting in the driver's seat at theoutset of a vehicle trip, such as by analyzing data from one or moresensors, such as vehicle mounted sensors; (2) collect telematics datafor that vehicle trip; (3) assign (and store) or associate thetelematics data for that vehicle trip to the individual within thehousehold that was identified as the driver during the vehicle trip; (4)determine a driving score for the individual and/or vehicle trip basedupon the one or more processors analyzing the telematics data for thevehicle trip; (5) update or build a vehicle-usage profile for thevehicle based upon the telematics data for the vehicle trip and/or thedriving score, the vehicle-usage profile indicating how much and whattime of day each member of a household typically drives or uses thevehicle, and their driving behavior while driving the vehicle (and/orotherwise accounting for driving behavior of each driver within ahousehold); and/or (6) update an auto insurance premium or discount forthe household or the vehicle based upon the vehicle-usage profile toprovide insurance cost savings to lower risk households and/or riskaverse drivers. The computer system may include additional, less, oralternate functionality, including that discussed elsewhere herein.

For instance, the one or more processors may be configured to transmit,via a transceiver, the updated auto insurance discount to the insuredfor their review and/or approval. The one or more processors may belocal to vehicle, such as mounted within a mobile device and/or mountedon or within the vehicle or a vehicle controller. The one or moreprocessors may be remote to the vehicle, such as a remote located serverassociated with an insurance provider.

FIG. 9 illustrates a flow diagram of an exemplary vehicle-usage profilegeneration method 900 for generating and/or updating a profile ofvehicle usage associated with a driver of a vehicle 108. The method 900may begin with determining the identity of the driver of the vehicle 108using sensor data or other means (block 902). Telematics data regardingthe operation of the vehicle 108 by the driver may then be collectedduring a vehicle trip (block 904). The collected data may be used togenerate or update a vehicle-usage profile associated with the vehicle108 and/or the driver (block 906). Based upon the vehicle-usage profile,an update or change to an insurance policy associated with the vehicle108 may be determined (block 908) and presented to the vehicle owner,driver, or insured party for review or approval (block 910). The method900 may be implemented by one or more external computing devices 206,such as one or more servers associated with an insurer.

At block 902, the external computing device 206 may determine theidentity of the driver of the vehicle 108. In some embodiments, theidentity may be first determined by a mobile computing device 110 oron-board computer 114 and transmitted to the external computing device206, which may then determine the identity based upon communicationsreceived from the mobile computing device 110 or on-board computer 114.The mobile computing device 110 or on-board computer 114 may determinethe driver of the vehicle 108 by comparison of sensor data from one ormore sensors, including microphones, digital optical cameras, infraredcameras, or similar sensors. The mobile computing device 110, on-boardcomputer 114, or external computing device 206 may instead determine theidentity of the driver by reference to an electronic signal generated bya device associated with the driver (e.g., a wearable computing device,a smartphone, a fitness tracker, etc.). In some embodiments, thelocation of one or more people within the vehicle 108 may be determinedto identify the driver by sensor data, electronic signal, or otherwise.In further embodiments, the driver may be identified by receiving amanual entry of an indication of the driver's identity (such as bylogging in or selecting a user within a Telematics App).

At block 904, the external computing device 206 may collect telematicsdata regarding vehicle operation while the vehicle 108 is being drivenby the identified driver. This may include one or more vehicle tripsover a period of time. The telematics data may include data regardingthe vehicle operating environment (e.g., information about a travelenvironment) or driving behavior (e.g., how the driver operates thevehicle with respect to speed, acceleration, braking, etc.). In someembodiments, the telematics data may be collected by the mobilecomputing device 110 or on-board computer 114 and transmitted to theexternal computing device 206 via the network 201. In furtherembodiments, the external computing device 206 may collect thetelematics data from one or more databases (or other data storagedevices) holding telematics data previously recorded by the mobilecomputing device 110 or on-board computer 114 (or by sensorscommunicatively connected thereto). In such manner, the externalcomputing device 206 may obtain telematics data regarding operation ofthe vehicle 108 by the driver. Such telematics data may be collectedfrom a plurality of vehicle trips occurring over a span of time (e.g.,one week, one month, etc.) to generate or update a vehicle-usageprofile.

At block 906, the external computing device 206 may generate or update avehicle-usage profile associated with the vehicle 108. In someembodiments, the vehicle-usage profile may indicate the amount eachdriver uses the vehicle (e.g., total or proportional time driving,vehicle trips, miles driven, etc.). This usage information may includeinformation regarding the type of vehicle operating environment ortravel environment in which the driver typically operates the vehicle108. The vehicle-usage profile may further include informationindicating the driving behavior of each driver of the vehicle 108. Suchdriving behavior information may include a driver score, a driverrating, or a driver profile indicating one or more risk levelsassociated with the manner in which the driver usually operates thevehicle 108 (e.g., whether or the degree to which the driver is riskaverse, aggressive, or inattentive while driving). This usage andbehavior information may be useful in accurately assessing risk forinsurance or other purposes when the vehicle 108 is regularly used by aplurality of drivers (e.g., a family car, a shared vehicle, etc.). Forexample, a vehicle-usage profile may indicate that a first driveroperates the vehicle 108 for a first amount of use (e.g., 25% of thetotal miles driven each month, 50 miles per week, 25 hours per month,etc.) in a first In one embodiment, this information may be aggregatedamong a plurality of drivers of a plurality of vehicles in avehicle-sharing group or network, such that profiles may be generatedfor each driver or each vehicle using information regarding the vehicleusage and driving behavior of drivers from multiple trips using multiplevehicles. When a vehicle-usage profile already exists, the externalcomputing device 206 may update the existing profile based upon newtelematics data, which updates may occur periodically or upon occurrenceof an event (e.g., new telematics data from a vehicle trip becomingavailable).

At block 908, the external computing device 206 may determine an updateor change to an insurance policy based upon the current vehicle-usageprofile. The update may include a change to a premium, a coverage level,a coverage type, an exclusion, an insured driver, or other aspects ofthe policy, as discussed elsewhere herein. Determining an update to aninsurance policy may include determining a change in one or more risklevels associated with operation of the vehicle 108 (or vehicleoperation by one or more drivers). This may include comparing currentvehicle-usage profiles with older vehicle-usage profiles containing dataprior to the update. For example, if an update to the vehicle-usageprofile reveals that a higher-risk driver now drives the vehicle 108fewer miles, a discount in proportion to the decreased risk may bedetermined. Some updates may include not changing any aspects of theinsurance policy, such as when a change in risk levels associated withvehicle operation are below a threshold for updating the insurancepolicy. Such thresholds may be used to avoid frequent changes of deminimis value.

At block 910, the update may be presented for review and/or approval.The external computing device 206 may cause the update to be presentedto the vehicle owner, vehicle operator, insured party, or otherinterested person or organization. The update may be presented via amobile computing device 110, on-board computer 114, or other externalcomputing device 206 (e.g., a home computer, tablet, laptop, etc.). Insome embodiments, the update may be presented for review and/or approvalprior to being implemented. In further embodiments, the update may beimplemented by applying the update to the insurance policy. Inappropriate cases, the external computing device 206 may facilitateappropriate payments or funds transfers between an insurer and aninsured related to the update.

FIG. 10 illustrates a flow diagram of another exemplary vehicle-usageprofile generation method 1000 for generating and/or updating a profileof vehicle usage associated with a driver of a vehicle 108. The method1000 may begin by receiving telematics data associated with theoperation of the vehicle 108 (block 1002). From this data, one or moredrivers may be identified (block 1004), and a driving score may bedetermined for each identified driver (block 1006). The vehicle-usageprofile may then be generated or updated based upon the determineddriving scores and usage levels for the one or more drivers (block1008). Based upon the vehicle-usage profile, an update or change to aninsurance policy associated with the vehicle 108 may be determined(block 1010) and presented to the vehicle owner, driver, or insuredparty for review or approval (block 1012). The method 1000 may beimplemented by one or more external computing devices 206, such as oneor more servers associated with an insurer.

At block 1002, the external computing device 206 may receive telematicsdata associated with operation of the vehicle 108. Telematics datarelating to a single vehicle trip may be received from the mobilecomputing device 110 or on-board computer 114 via the network 201. Forexample, the mobile computing device 110 or on-board computer 114 mayautomatically upload the telematics data to the external computingdevice 206 (or a data storage associated therewith) upon completion of avehicle trip (or at points during the trip). Alternatively, the externalcomputing device 206 may receive telematics data for a plurality ofvehicle trips, which may or may not include multiple drivers. In someembodiments, the external computing device 206 may request and receivedata from a database or other data storage mechanism. For example, theexternal computing device 206 may request only new data since a previousupdate of the vehicle-usage profile from a database.

At block 1004, the external computing device 206 may determine thedriver or drivers associated with the received telematics data. This mayinclude determining one or more vehicle trips included in the receivedtelematics data, which may be achieved by reference to vehiclemovements, time-stamps, indications of engine start-up or shut-down,etc. As noted elsewhere herein, the driver for each trip or portion of atrip may be determined by sensor data, electronic signals, or othermeans. The external computing device 206 may, therefore, associate adriver with each vehicle trip or portion thereof that is included in thereceived data.

At block 1006, the external computing device 206 may determine a drivingscore for each of the identified drivers based upon the receivedtelematics data. The driving score may indicate a level of driving skillor a risk level associated with the driving behavior of the driver. Forexample, the driving score may be adjusted from a baseline for positiveor negative driving behaviors identified from the telematics data. Forexample, a driver may have a baseline of 80 points, from which 5 pointsmay be subtracted for driving in heavy traffic conditions, 2 points maybe subtracted for following too closely behind other vehicles, one pointmay be added for consistent use of turn signals, etc. The baseline maybe a general baseline or may be the driver's previous cumulative scoreprior to the update. Alternatively, the driving score may indicate arisk level associated with operation of the vehicle 108 by the driver,which may be based upon risks of both the driving behavior of the driverand the vehicle operating environment. In some embodiments, the drivingscore may include a plurality of sub-scores indicating aspects ofdriving behavior or an assessment of driving behavior in differenttravel environments.

At block 1008, the external computing device 206 may generate or updatea vehicle-usage profile based upon the driving scores determined for theone or more drivers and the level of vehicle usage of each driver. Asabove, the vehicle-usage profile may indicate the amount each driveruses the vehicle, the typical vehicle operating environment for eachdriver, and an indication of each driver's driving score. In someembodiments, indications of risk levels associated with each driver maybe included in the vehicle-usage profile. When a vehicle-usage profilealready exists, the external computing device 206 may simply update theexisting profile based upon new telematics data.

At block 1010, the external computing device 206 may determine an updateor change to an insurance policy based upon the current vehicle-usageprofile. The update may include a change to a premium, a coverage level,a coverage type, an exclusion, an insured driver, or other aspects ofthe policy, as discussed elsewhere herein. Determining an update to aninsurance policy may include determining a change in one or more risklevels associated with operation of the vehicle 108 (or vehicleoperation by one or more drivers). The driving scores may be used todirectly or indirectly determine risks associated with each driver forpurposes of determining updates or changes to the insurance policy.

At block 1012, the external computing device 206 may further implementthe update to the insurance policy. This may include causing informationassociated with the update or change to be presented to the vehicleowner, driver, insured party, or other interested person or organizationfor review and/or approval. The external computing device 206 mayfurther effect the implementation of the update by generating oradjusting records relating to the policy terms. In appropriate cases,the external computing device 206 may facilitate appropriate payments orfunds transfers between an insurer and an insured related to the update.

Traffic Condition Broadcast

In one aspect, a computer-implemented method of generating trafficalerts and abnormal traffic condition avoidance may be provided. Themethod may include (1) detecting, via one or more processors (such asvehicle-mounted sensors or processors), that an abnormal trafficcondition exists in front of the vehicle or in the vicinity of thevehicle; (2) generating, via the one or more processors, an electronicmessage detailing the abnormal traffic condition; and/or (3)transmitting, via the one or more processors (or an associatedvehicle-mounted transceiver), the electronic message to nearby vehicles(and/or their associated vehicle controller/processors, such as withautonomous or self-driving vehicles) traveling behind the vehicle viawireless communication or data transmission to alert other drivers ofthe abnormal traffic condition and to allow them to avoid the abnormaltraffic condition to facilitate safer vehicle travel. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

For instance, the abnormal traffic condition may be (1) an erraticvehicle or driver; (2) road construction; (3) closed highway exit; (4)slowed or slowing traffic or congestion; and/or (5) vehicles brakingahead. The abnormal traffic condition may be bad weather (rain, sleet,snow, ice, freezing rain, etc.), and the message may indicate a GPSlocation of the bad weather. The method may include generating, via theone or more processors or a remote processor (e.g., smart infrastructureor remote server), an alternate route for nearby vehicles to take toavoid the abnormal traffic condition. The method may include generating,via the one or more processors or a remote processor, an auto insurancediscount for the vehicle having the abnormal traffic condition detectionfunctionality and broadcasting the electronic message indicating theabnormal traffic condition.

In another aspect, a computer-implemented method of generating trafficalerts and providing for abnormal traffic condition avoidance may beprovided. The method may include (1) detecting, via one or moreprocessors (such as vehicle-mounted sensors or processors), that anabnormal traffic condition exists, such as via analysis of vehicletelematics data (e.g., determining vehicle traveling through roadconstruction or congestion); (2) generating, via the one or moreprocessors, an electronic message detailing the abnormal trafficcondition; and/or (3) transmitting (such as transmitting only when theabnormal traffic condition exists to conserve energy), via the one ormore processors (or an associated vehicle-mounted transceiver), theelectronic message to nearby vehicles (and/or their associated vehiclecontroller/processors, such as with autonomous or self-driving vehicles)traveling behind the vehicle via wireless communication or datatransmission to alert other drivers of the abnormal traffic conditionand to allow them to avoid the abnormal traffic condition to facilitatesafer vehicle travel. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In one aspect, a computer system configured to generate traffic alertsand provide for abnormal traffic condition avoidance may be provided.The computer system comprising one or more processors and/ortransceivers (such as vehicle-mounted processors or sensors), the one ormore processors configured to: (1) detect that an abnormal trafficcondition exists in front of the vehicle; (2) generate an electronicmessage detailing the abnormal traffic condition; and/or (3) transmit,via an associated vehicle-mounted transceiver, the electronic message tonearby vehicles (and/or their associated vehicle controller/processors,such as with autonomous or self-driving vehicles) traveling behind thevehicle via wireless communication or data transmission to alert otherdrivers of the abnormal traffic condition and to allow them to avoid theabnormal traffic condition to facilitate safer vehicle travel. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

For instance, the abnormal traffic condition may be (1) an erraticvehicle or driver; (2) road construction; (3) closed highway exit; (4)slowed or slowing traffic or congestion; and/or (5) vehicles brakingahead. The abnormal traffic condition may be bad weather (rain, sleet,snow, ice, freezing rain, etc.), and the message indicates a GPSlocation of the bad weather.

The system may be configured to generate, via the one or more processorsor a remote processor (e.g., smart infrastructure or remote server), analternate route for nearby vehicles to take to avoid the abnormaltraffic condition. The system may be configured to generate, via the oneor more processors or a remote processor, an auto insurance discount forthe vehicle having the abnormal traffic condition detectionfunctionality.

In another aspect, a computer system configured to generate trafficalerts and provide for abnormal traffic condition avoidance may beprovided. The computer system may include one or more vehicle-mountedprocessors and/or sensors configured to: (1) detect that an abnormaltraffic condition exists, such as via analysis of vehicle telematicsdata (e.g., determining vehicle traveling through road construction orcongestion); (2) generate an electronic message detailing the abnormaltraffic condition; and/or (3) transmit (such as transmitting only whenthe abnormal traffic condition exists to conserve energy), via anassociated vehicle-mounted transceiver, the electronic message to nearbyvehicles (and/or their associated vehicle controller/processors, such aswith autonomous or self-driving vehicles) traveling behind the vehiclevia wireless communication or data transmission to alert other driversof the abnormal traffic condition and to allow them to avoid theabnormal traffic condition to facilitate safer vehicle travel.

FIGS. 11 and 12 illustrate methods of determining, generating,transmitting, receiving, and presenting alerts or notifications relatedto anomalous or abnormal conditions. The exemplary method illustrated inFIG. 11 may be used to detect such anomalous conditions and to generateand transmit messages to inform other vehicles. The exemplary methodillustrated in FIG. 12 may be used to receive, parse, and present alertsbased upon such messages. Although the methods are described as beingperformed by mobile computing devices 110 and/or on-board computers 114associated with vehicles, some embodiments may instead perform some orall actions using one or more external computing devices 206, such asservers communicatively connected to the vehicles via the network 201.

FIG. 11 illustrates a flow diagram of an exemplary traffic conditionbroadcast method 1100 for detecting anomalous conditions in a vehicleoperating environment and generating electronic messages to warn othervehicles. The method 1100 may begin by collecting sensor data from oneor more sensors within the vehicle operating environment of a vehicle108 (block 1102). The sensor data may be processed to detect whether anyanomalous conditions exist (block 1104). When an anomalous condition isdetected, an appropriate electronic message may then be generatedregarding the anomalous condition (block 1106). The electronic messagemay then be transmitted to nearby vehicles 202 via wirelesscommunication to warn such other vehicles 202 of the anomalous condition(block 1108).

At block 1102, the vehicle 108 may collect sensor data regarding theoperating environment through which the vehicle 108 is traveling. Thesensor data may be collected by a mobile computing device 110 oron-board computer 114 associated with the vehicle 108. The sensor datamay be collected from one or more sensors disposed within the vehicle108 (including sensors disposed within the mobile computing device 110located within the vehicle 108). The sensors may be built into thevehicle 108, the mobile computing device 110, or the on-board computer114, or the sensors may be communicatively connected to the mobilecomputing device 110 or on-board computer 114 via wired or wirelessconnections. The sensors may monitor events within the vehicle 108(e.g., acceleration, speed, sounds, etc.) or outside the vehicle 108(e.g., number and location of other vehicles, movement of othervehicles, number of pedestrians near the vehicle 108, weatherconditions, road integrity, construction, lane closures, etc.). In someembodiments, sensor data may be collected from or by smartinfrastructure components 208. Such infrastructure components 208 mayinclude sensors to generate sensor data regarding vehicle trafficpassing a position (e.g., vehicles passing through a toll booth, vehiclepassing an embedded sensor in a roadway, state of a railroad crossinggate, etc.), pedestrian traffic (e.g., number of pedestrians on asidewalk, state of a pedestrian-activation button on a cross-walksignals, etc.), atmospheric conditions (e.g., temperature,precipitation, wind, etc.), or other data regarding a local environment.Sensor data from infrastructure components 208 may be transmittedwirelessly to a mobile computing device 110 or on-board computer 114 ofa nearby vehicle, may be transmitted via the network 201 to an externalcomputing device 206, or may be processed by the infrastructurecomponent 208 to generate an alert concerning an anomalous condition. Infurther embodiments, sensor data may be transmitted between vehicles toimprove safety (e.g., vehicles may transmit indications of suddenbraking to warn nearby vehicles).

At block 1104, the mobile computing device 110 or on-board computer 114may determine whether an anomalous condition exists based upon thereceived sensor data. Such anomalous conditions may include anomaloustraffic conditions (e.g., traffic jams, accidents, heavy trafficcongestion, lane closures, ramp closures, potholes, slowing traffic,sudden braking, erratic vehicle operation, swerving, etc.), anomalousweather conditions (e.g., high winds, road ice, heavy precipitation,flooding, etc.), anomalous environmental conditions (e.g., heavypedestrian traffic, pedestrians on the roadway, heavy bicycle traffic,wild animals on or near the roadway, etc.), or other similar anomalousconditions relating to the vehicle operating environment through whichthe vehicle 108 is traveling. For example, an accident may result in acongestion and slowing of traffic in the area of the accident. This maybe detected based upon sensor data regarding vehicle braking, vehiclespeed, number of vehicles on a segment of the roadway, and/or distancebetween vehicles. Based upon the sensor data, the occurrence of atraffic jam and/or accident may be detected by the mobile computingdevice 110 or on-board computer 114. As another example, heavypedestrian traffic at the end of a sporting event or concert may lead toslow vehicle traffic flow and increased risk of a collision in an area.Sensor data may indicate an unusually high number of pedestrians in thearea, as well as slow vehicle movement. This sensor data may be assessedby the mobile computing device 110 or on-board computer 114 to determinethat an anomalous condition exists as a result of heavy pedestriantraffic. Alternatively, an external computing device 206 may detectheavy pedestrian traffic based upon GPS data from mobile devices ofnumerous pedestrians in the area. As yet another example, the vehicle108 may detect ice on the roadway based upon sensor data indicating amomentary or partial loss of traction by the vehicle 108. In someembodiments, a plurality of anomalous conditions may be detected basedupon the same sensor data.

At block 1106, the mobile computing device 110 or on-board computer 114may generate an electronic message based upon the detected anomalouscondition. The message may include information regarding the type andlocation of the anomalous condition. For example, the message mayindicate an accident has occurred and a location of the vehicle 108. Themessage may include an indication of severity or urgency of theanomalous condition or an indication of the duration of the condition.For example, a flooded roadway or closed bridge may be flagged as highimportance or extremely urgent conditions, whereas a lane closure orslow-moving traffic may be left unlabeled or labeled as low-priorityanomalous conditions. In some embodiments, the electronic message may beencoded for security or may use a standardized format for datatransmission. In further embodiments, the message may includeinformation regarding the anomalous condition (or other anomalousconditions in the vicinity of the vehicle 108) received by the vehicle108 in one or more electronic messages from other vehicle 202. In suchmanner, the information regarding the anomalous condition may beautomatically generated and updated in real time as a plurality ofvehicles reach the location or communicate additional information.

At block 1108, the mobile computing device 110 or on-board computer 114of the vehicle 108 may transmit the electronic message to other nearbyvehicles 202. In some embodiments, the vehicle 108 may transmit theelectronic message on a dedicated frequency using one-way communicationsto any other vehicles that may be in the vicinity. In other embodiments,the vehicle 108 may transmit the electronic message (directly orindirectly) to identified vehicles 202 near the vehicle 108. The vehicle108 may similarly transmit the electronic message to an externalcomputing device 206 via the network 201, which external computingdevice 206 may redirect the electronic message to appropriate vehicles202 via the network 201. Where other vehicles 202 are specificallytargeted to receive the electronic message from the vehicle 108, thevehicles 202 may be selected based upon proximity (e.g., as determinedby comparison of GPS coordinates) or based upon an anticipated path ofthe vehicles 202. For example, the vehicle 108 may not transmit theelectronic message to other vehicles 202 that are near the anomalouscondition but are also moving away from the anomalous condition, such asvehicles 202 that have already passed the site of an accident. Instead,the vehicle 108 may transmit the electronic message (directly orindirectly) to other vehicles 202 that are further from the accidentsite but are traveling toward it. In other embodiments, the vehicle 108may not distinguish between vehicles 202 based upon their path, withsuch determination of relevance being left to the vehicle 202 receivingthe message.

FIG. 12 illustrates a flow diagram of an exemplary alert generation andpresentation method 1200 for receiving electronic messages concerninganomalous conditions in the vehicle operating environment and takingactions based upon the contents such messages. The method 1200 may beginby receiving at a vehicle 202 an electronic message indicating theoccurrence of an anomalous condition via wireless communication (block1202). The electronic message may then be processed to extractinformation regarding the anomalous condition from the message data(block 1204). This information regarding the anomalous condition may beused to determine a response, which may include generating an alertand/or recommendation based upon the information (block 1206). The alertand/or recommendation may be presented to the driver of the vehicle 202and/or implemented by a vehicle control or navigation system (block1208). In some instances, the electronic message may be retransmitted(with or without modification) to other nearby vehicles to disseminatethe information regarding the anomalous condition (block 1210).

At block 1202, the vehicle 202 may receive the electronic messageregarding the one or more detected anomalous conditions via wirelesscommunication, such as electronic messages generated and transmitted asdescribed above. The electronic message may be received directly orindirectly from a vehicle 108 generating the message, as discussedabove, or the electronic message may be received from an externalcomputing device 206 (such as a server associated with an insurer,navigation service, or travel alert service). The electronic message maybe received by an antenna of the vehicle 202 or via a networkcommunication connection of the mobile computing device 110 or on-boardcomputer 114. As noted above, the vehicle 202 may also receiveelectronic messages from smart infrastructure components 208 in someembodiments. In some embodiments, the message may be received fromtransmitters associated with locations or vehicles of particularsignificance. For example, slow-moving vehicles (e.g., farm machinery,construction equipment, oversized load vehicles, etc.) or emergencyvehicles (e.g., ambulances, fire trucks, police vehicles, etc.) may beequipped to transmit electronic messages indicating their presence tonearby vehicles 202. Similarly, portable communication devices may beused by pedestrians, cyclists, or others to notify vehicles of theirpresence by transmitting electronic messages.

At block 1204, the mobile computing device 110 or on-board computer 114may process the received electronic message to extract informationregarding the anomalous condition. This may include determining types ofanomalous conditions, locations associated with the anomalousconditions, indications of urgency or importance of the message, and/ora time associated with the message. In some embodiments, this may alsoinclude determining multiple messages from vehicles 108 includeinformation regarding the same anomalous condition (or aspects thereof).For example, a first message may be received regarding a traffic back-upbeginning at a first point along the roadway, and a second message maybe received indicating a lane closure or accident at a second pointfurther along the roadway.

At block 1206, the mobile computing device 110 or on-board computer 114may determine an alert or recommendation regarding the anomalouscondition. Determining alerts may include determining the level ofdetail to present to the driver of the vehicle 202 regarding theanomalous condition. For example, an icon may be presented on a mappresented by a navigation system to indicate the location of ananomalous condition, which may include an indication of the general typeof condition. In other embodiments, a warning may be presented to thedriver, which may include information regarding the type and location ofthe anomalous condition. When messages regarding multiple anomalousconditions are received, the mobile computing device 110 or on-boardcomputer 114 may determine which and how many alerts to present. Infurther embodiments, the mobile computing device 110 or on-boardcomputer 114 may determine one or more recommendations regarding vehicleoperation in view of the information received regarding one or moreanomalous conditions. For example, an alternative route may be suggestedto the driver to avoid a traffic jam or construction. As anotherexample, an alert may include a recommendation to seek shelter when asevere storm is approaching.

In some embodiments, determining the alert or recommendation may includedetermining whether the anomalous condition is relevant to the vehicle202. This may include comparing the location of the anomalous conditionwith a projected path of the vehicle 202. The projected path may bedetermined by simply following the road on which the vehicle 202 istraveling in the direction of travel, or it may be determined from anavigation system providing directions to a destination. In someembodiments, past vehicle travel may be used to project one or moreprobable paths for the vehicle 202. Anomalous conditions not fallingwithin the vehicle path may be deemed irrelevant and suppressed in someembodiments. For example, an accident located on the same road butbehind the vehicle 202 may be of little importance and may be ignored bythe mobile computing device 110 or on-board computer 114.

At block 1208, the mobile computing device 110 or on-board computer 114may cause the determined alert or recommendation to be presented to thedriver of the vehicle 202. This may include causing a visual warning tobe presented on a screen associated with the mobile computing device 110or on-board computer 114, an audible warning to be presented by aspeaker, or other types of warnings to alter the driver of the anomalouscondition. Recommendations may similarly be presented to the driver,such as by presentation of text on a screen or as spokenrecommendations. In some embodiments, a recommendation may beautomatically implemented, with or without notice to the driver. Forexample, a navigation system may automatically update directions tofollow a recommended alternate route. As another example, an autonomousvehicle may automatically follow an alternate route based upon therecommendation determined by the mobile computing device 110 or on-boardcomputer 114.

At block 1210, the mobile computing device 110 or on-board computer 114may determine to retransmit the received electronic message to othernearby vehicles 202. In some embodiments, this may include addingadditional information regarding the anomalous condition available tothe vehicle 202 to the original electronic message. In otherembodiments, the vehicle 202 may determine not to retransmit theelectronic message. For example, the range of the original transmissionmay be deemed sufficient to alert all vehicles in the vicinity of theanomalous condition, or an external computing device 206 may direct theelectronic message to other relevant vehicles. In some embodiments, thevehicle 202 may determine to retransmit the electronic message onlyafter a delay period, which may facilitate distribution of the messageto additional vehicles just entering the transmission range. Forexample, an electronic message regarding the beginning of a traffic jammay be delayed by fifteen seconds, such that faster moving vehiclesapproaching from behind may enter the transmission range in the intervalbetween the original message transmission and the messageretransmission.

Using Personal Telematics Data for Rental/Insurance Discounts

In one aspect, a computer-implemented method of using telematics dataduring e-commerce may be provided. The method may include (1)collecting, via one or more processors associated with a customer,telematics data detailing the customer's typical driving behavior; (2)transmitting, via a transceiver under the direction or control of theone or more processors, the telematics data directly or indirectly to arental car company remote server, such as via wireless communication ordata transmission; and/or (3) receiving, via the transceiver under thedirection or control of the one or more processors, a computer generateddiscount off the rental price associated with renting a rental vehiclefrom the rental car company to facilitate (i) rewarding safe drivingwith lower cost rental vehicles for risk averse drivers, and/or (ii)allow consumers to collect their own telematics data and enjoy costsavings if they decide to share their telematics data with variousmerchants.

In another aspect, a computer-implemented method of using telematicsdata during e-commerce may be provided. The method may include (1)receiving, via one or more processors, telematics data detailing thecustomer's typical driving behavior; (2) determining, via the one ormore processors, a discount for a rental car from computer analysis ofthe customer's telematics data; and/or (3) transmitting, via atransceiver under the direction or control of the one or moreprocessors, the rental car discount to a customer's mobile device, suchas via wireless communication or data transmission, for the customerreview and/or approval to facilitate (i) rewarding safe driving withlower cost rental vehicles for risk averse drivers, and/or (ii) allowconsumers to collect their own telematics data and enjoy cost savings ifthey decide to share their telematics data with various merchants. Theforegoing methods may include additional, less, or alternatefunctionality, including that discussed elsewhere herein and/or may beimplemented via one or more local or remote processors.

FIG. 13 illustrates a flow diagram of an exemplary personal telematicsdata profile generation method 1300 for generating a driving behaviorprofile used for rental discounts. The method 1300 may be used by adriver to record, store, and share telematics data to obtain a discountfor safe driving when renting a vehicle, such as a rental car or movingtruck. The method 1300 may begin with the collection of telematics dataregarding vehicle operation by a driver (block 1302). The telematicsdata may be transmitted to a remote server for analysis (block 1304).Based upon the transmitted telematics data, the server may determine orgenerate a driving behavior profile associated with the driver (block1306). A discount on a vehicle rental may further be determined basedupon the driving behavior profile (block 1308). The discount may then bepresented to the user and/or applied to the rental fee (block 1310).

At block 1302, telematics data may be collected using sensors disposedwithin or communicatively connected to the mobile computing device 110or on-board computer 114. In some embodiments, the telematics data maybe collected by a Telematics App. The telematics data may be any datarelating to the usage, operating environment, or operation of thevehicle associated with the driver (e.g., time, location, weather,traffic, type of road, pedestrian traffic, geographic area, speed,braking, acceleration, swerving, lane centering, distance from othervehicles, etc.). The telematics data may be stored within the mobilecomputing device 110 or on-board computer 114, or the telematics datamay be stored in a remote data storage device (e.g., in a databaseassociated with a server or in a cloud computing data storage). Thetelematics data may be stored as a collection of data points, or it maybe preprocessed into a summary form. For example, the data may besummarized into a set of scores related to aspects of the driver'sbehavior when operating the vehicle 108 (e.g., acceleration, braking,steering, traffic level, type of road, etc.). As another example,summary data regarding locations, times, and durations of vehicleoperation may be recorded. As yet another example, summary dataregarding only anomalous conditions or high-risk driving behavior may berecorded to reduce the amount of data stored. In some such situations, asummary of driving without anomalous or high-risk conditions may also bestored (e.g., total miles driven, total time driven, etc.). Suchtelematics data may be associated with all vehicle operation by thedriver or may be limited to previous rental vehicle operation by thedriver.

At block 1304, the collected telematics data may be transmitted to aremote server (such as an external computing device 206) via the network201. In some embodiments, the remote server may be associated with,operated by, or operated on behalf of an insurer, a vehicle rentalcompany, or a third-party rating agency. A driver may operate aTelematics App installed on the mobile computing device 110 or on-boardcomputer 114 to transmit the telematics data to the remote server. TheTelematics App may cause the telematics data (or a summary thereof) tobe transmitted from the mobile computing device 110 or on-board computer114 to the remote server in some embodiments. In other embodiments inwhich the telematics data is stored in a cloud storage or other remotestorage device, the Telematics App may cause the transmission of thetelematics data from such storage device to the remote server.Alternatively, the Telematics App may retrieve the telematics data fromthe storage device and retransmit the telematics data to the remoteserver. In further embodiments, the telematics data may be transmittedin response to a request from the remote server. In yet furtherembodiments, the telematics data may be automatically transmitted to theremote server by the Telematics App Such automatic transmission mayoccur either periodically or upon occurrence of an event, such as whennew data is available and the mobile computing device 110 or on-boardcomputer 114 has a sufficient network connection or when a vehicle isreturned to a vehicle rental facility.

At block 1306, the remote server may determine a driving behaviorprofile for the driver based upon the telematics data. The drivingbehavior profile may include indications of risk levels associated withvehicle operation by the driver in one or more sets of operatingconditions (e.g., vehicle operating environments, travel environments,geographic locations, weather conditions, etc.). Such indications ofrisk levels may include risk scores or risk categories associated withthe driver. In some embodiments, the driving behavior profile mayinclude only summary information regarding vehicle operation by thedriver, while other embodiments may include detailed informationregarding such vehicle operation. The remote server may determine therisk levels by comparing the received telematics data against known lossrate associated with other drivers having similar driving habits orpatterns (based upon the received telematics data). This may includeanalyzing the telematics data using machine learning algorithms orapplying pre-determined statistical models to the telematics datareceived. Although the foregoing discussion describes the drivingbehavior profile as being determined by an external computing device 206beyond the control of the driver, some embodiments may include using thedriver's mobile computing device 110 (such as a smartphone) or on-boardcomputer 114 to determine the driving behavior profile. In suchembodiments, a Telematics App or other program installed on the mobilecomputing device 110 or on-board computer 114 may determine the drivingbehavior profile. Such driving behavior profile may then be transmittedto an external computing device 206 associated with the vehicle rentalfacility or may present a summary report that may be displayed whenrenting a vehicle.

At block 1308, the external computing device 206 may determine adiscount for the vehicle rental based upon the driving behavior profile.The discount may be determined based upon risk levels or categoriesindicated by the driving behavior profile to reflect the loss associatedwith the driver's operation of the rented vehicle. The appropriatediscount based upon the indicated risk may be determined by appropriateactuarial methods based upon loss data from a plurality of drivers forwhom telematics data or driving behavior profiles are available. In someembodiments, this may be supplemented with telematics or loss dataspecific to rental vehicles similar to that being rented by the driver.Such supplemental data may reflect differences in risk levels for rentalvehicles, which may be of different design or have different operatingcharacteristics from vehicle ordinarily operated by the driver. In someembodiments, the discount may include a reduction in rental price, areduction in a premium on an insurance policy covering the rentalvehicle, a lower deductible on such an insurance policy, an expandedcoverage on such an insurance policy, or an additional coverage typeavailable to the driver. In further embodiments, increases in rentalprice, premiums, or deductibles may be determined based upon the drivingbehavior profile, or the vehicle rental facility may require a minimumlevel of insurance based upon the driving behavior profile. In otherembodiments, a guaranteed minimum discount level may be offered to thedriver in exchange for providing telematics data or a driving behaviorprofile to the vehicle rental facility.

At block 1310, the discount may be presented for review and/or appliedto a vehicle rental. The discount may be presented to the driver as partof a quoted price or as part of a plurality of options presented forselection when arranging to rent a vehicle. For example, each of aplurality of options indicating a price without a discount, a discountbased upon the determined driving behavior profile, and a discountedprice may be presented to the driver (e.g., a plurality of vehicles,days, or insurance options). Alternatively, the discount may beautomatically applied to the rental price when the vehicle is rented. Instill further embodiments, the discount may be applied when the rentedvehicle is returned, and such discount may be based upon a drivingbehavior profile specific to operation of the rented vehicle by thedriver.

Shared Vehicle Usage Monitoring and Feedback

In one aspect, methods and systems for monitoring and providing feedbackregarding vehicle operation may be provided. Such feedback may be usefulfor monitoring usage of shared vehicles or fleet management. The methodsand systems may be used to monitor operation of a vehicle by a driverother than the vehicle owner, such as drivers of family vehicles, rentalvehicles, car-share vehicles, or company vehicles (e.g., delivery vans,company car pool vehicles, etc.). In some embodiments, the methods andsystems may operate in real-time or near real-time to providenotifications or metrics during vehicle operation. As an example, aparent may receive real-time notifications of aggressive driving of afamily vehicle by a child, which notifications may be pushed to a mobilephone of the parent by SMS text messages or application-specificnotifications.

FIG. 14 illustrates a flow diagram of an exemplary shared vehicle usagemonitoring method 1400 for reporting vehicle usage information to avehicle owner or other interested party. The method 1400 may collecttelematics data related to vehicle operation of a vehicle 108 (block1402), which data may be associated with a driver or period of vehicleusage (block 1404). From such collected telematics data, one or moredriving events may be identified (block 1406). Such driving events maybe events indicative of improper driving behavior and/or prohibitedvehicle usage. Based upon the identified driving events, a notificationor report may be generated regarding operation of the vehicle 108 (block1408). The notification or report may then be transmitted to aninterested party, such as a vehicle owner (block 1410).

At block 1402, telematics data regarding vehicle operation of thevehicle 108 may be collected using one or more sensors. Such sensors mayinclude accelerometers, speedometers, tachometers, geopositioningdevices, cameras, or other sensors disposed within the vehicle 108(including sensors within a mobile computing device 110 within thevehicle), within other nearby vehicles 202 in proximity to the vehicle108, and/or within the vehicle operating environment of the vehicle 108(e.g., sensors disposed within smart infrastructure components 208). Thetelematics data may be collected locally by a mobile computing device110 or on-board computer 114 within the vehicle 108, or the telematicsdata may be collected by an external computing device 110 (such as aremote server associated with an insurer, the vehicle owner, or athird-party monitoring service). The telematics data may be collected,recorded, and/or transmitted using a Telematics App of the mobilecomputing device 110 or on-board computer 114. In some embodiments, theTelematics App may receive sensor data and transmit the sensor data (ora summary thereof) to a remote server for further analysis. Regardlessof the site of collection, the telematics data may include informationregarding the times, locations, and manners of vehicle operation. Forexample, information regarding speed, braking, acceleration, mobilephone usage, turn signal usage, or similar indication of drivingbehavior may be included in the telematics data. The telematics data mayfurther include data indicating one or more drivers operating thevehicle 108.

At block 1404, the mobile computing device 110, on-board computer 114,or external computing device 206 may determine a driver and/or timeperiod of vehicle operation based upon the received data and may furtherassociate the determined driver and/or time period with the telematicsdata. This may include determining the identity of the driver based uponsensor data (e.g., an image from a camera), electronic signals (e.g., anidentifying signal from a smartphone), or other means (e.g., driverselection of a user identity in a Telematics App when beginningoperation). The time period may be determined by reference to aninternal clock of the mobile computing device 110, on-board computer114, or external computing device 206 or comparison of a date and timeof the telematics data (e.g., a timestamp) with a schedule of dates ortimes to determine a relative time period (e.g., daylight hours,twilight, night, weekdays, holidays, etc.). The driver and/or timeperiod of vehicle operation may be associated with the telematics dataregarding the operation of the vehicle 108.

At block 1406, the mobile computing device 110, on-board computer 114,or external computing device 206 may identify one or more driving eventsindicative of improper or prohibited driving behavior based upon thereceived telematics data. This may include comparing vehicle usage withinformation regarding allowed or prohibited uses for the identifieddriver and/or time period. For example, a child driving a family vehiclemay be prohibited from driving outside a predetermined geographic areaor during certain nighttime hours (which may differ on weekends orholidays), but another child driving the same family vehicle may havedifferent restrictions. As another example, an employee driving a fleetvehicle may be prohibited from driving on highways or deviating by morethan a predetermined distance from a set route. As yet another example,use of a car-sharing vehicle for extended trips (e.g., multi-day trips,trips beyond 100 miles, etc.) may violate restrictions on use of theshared vehicle. Operation of the vehicle 108 beyond the allowedoperating environment (or in violation of prohibitions on operation) maybe identified by the mobile computing device 110, on-board computer 114,or external computing device 206 as a driving event indicatingprohibited vehicle operation.

Similarly, the mobile computing device 110, on-board computer 114, orexternal computing device 206 may identify improper vehicle operationbased upon the telematics data regarding the driving behavior of thedriver. Such improper driving behavior may include actions by the driverwhile operating the vehicle 108 that increase the risk of an accident orviolate legal rules governing vehicle operation, such as one or more ofthe following: excessive acceleration, excessive speed, hard braking,sharp turning, rolling stops, tailgating, lane departure, swerving, orapproaching too near another vehicle 202 during operation of the vehicle108. For example, driving more than a threshold level above a speedlimit, sharp braking, excessive acceleration, rolling stops, or lateralswerving may be identified as indicative of improper driving behavior.Use of a mobile phone during vehicle operation, hands-free operation ina construction zone or heavy traffic conditions, or similar distractionsmay also be identified as improper driving behavior. Excessive vehicleoperation within a period of time (e.g., driving more than fourteenhours in a day, driving more than eleven consecutive hours without asubstantial break, etc.) may also be identified as a driving eventindicative of improper vehicle operation. Driving events may be assigneda level of urgency, such that driving events associated withparticularly high-risk driving behavior may be indicated as being moreurgent than driving events associated with lower-risk driving behavior.

At block 1408, the mobile computing device 110, on-board computer 114,or external computing device 206 may generate one or more notifications,alerts, or reports based upon the determined driving events. Alerts ornotifications may be generated in real-time (i.e., while vehicleoperation is ongoing) or when a vehicle trip has been completed. Somedriving events may trigger alerts or notifications based upon associatedurgency levels or types of driving events, while other types orlower-urgency driving events may not trigger alerts or notifications. Insome embodiments, a cumulative score may be maintained during vehicleoperation, in which case an alert or notification may be triggered whenthe score exceeds or drops below threshold levels. Such score mayinclude a driver rating, and the threshold levels may vary according tothe driver, time, location, or other factors involved. In someembodiments, a report may be generated periodically, upon completion ofa vehicle trip, or upon request by a user (such as a vehicle owner). Thereport may include information regarding vehicle operation, includingdriving events. In this way, driving events that may not trigger analert or notification may nonetheless be included in a report regardingvehicle operation by the driver.

At block 1410, the mobile computing device 110, on-board computer 114,or external computing device 206 may transmit the alert, notification,or report to an interested party. The interested party may be a vehicleowner, a fleet manager, an insurer, other shared-vehicleowners/stakeholders, a vehicle rental facility, or other parties with aninterest in the vehicle 108. Alerts or notifications may be sent inreal-time for some driving events or when cumulative driving behaviorbecomes unacceptable, as described above. This may include sending SMStext messages, automated phone calls, e-mail messages, or push alertsvia an application or program installed upon a mobile device orcomputing device of the interested party. Similarly, reports may beautomatically transmitted to the interested party periodically (e.g.,daily, weekly, etc.) or upon occurrence of an event (e.g., at theconclusion of a vehicle trip, upon return of the vehicle 108 to one ormore predetermined parking or garaging locations, etc.).

In some embodiments, the interested party may be presented with anoption to present immediate feedback to the driver. This may includesending messages or images to be presented to the driver via the mobilecomputing device 110 or on-board computer. Such messages may bepredefined (e.g., “slow down”) or may be entered by the interestedparty. This may also include an option to establish a voicecommunication connection (such as a mobile telephony connection) withthe driver. In some embodiments, the interested party may similarly havean option to provide more general feedback or ratings for drivers basedupon a driver report. Such feedback or ratings may be particularlyuseful in vehicle-sharing or vehicle rental contexts, where futurerentals may be accepted or rejected based upon the feedback.

Driver Evaluation and Warnings

In one aspect, systems and methods for facilitating driver evaluationsand warnings based upon such driver evaluations may be provided. Suchdriver evaluations may be solicited or otherwise obtained from otherdrivers, thereby providing a more complete assessment of the evaluateddriver's driving behavior. The systems and methods disclosed herein mayfurther provide for feedback to the evaluated driver to help improvedriving behavior. The driver evaluations may be combined with other datarelating to a driver or vehicle, from which a driving score or profilemay be generated. Driving scores or profiles may then be used to alertother drivers nearby the evaluated driver when the driver poses asignificant risk of causing a vehicle accident. In addition to otherbenefits relating to risk assessment and warnings, the systems andmethods disclosed herein may reduce risk by reducing road rage byproviding a means of reporting negative driving behavior.

FIG. 15 illustrates a flow diagram of an exemplary driver evaluationmethod 1500 for facilitating driver evaluation of the operation of othervehicles 202 in the vicinity of the driver of a vehicle 108. The method1500 may begin with monitoring other vehicles 202 within the vehicleoperating environment of the vehicle 108 (block 1502) and identifying atarget vehicle 202.1 to evaluate (block 1504). Data related to theoperation of the target vehicle 202.1 may be recorded for use inidentifying and/or evaluating the target vehicle 202.1 (block 1506). Anoption to evaluate the target vehicle 202.1 may be presented to thedriver of the vehicle 108 (block 1508), and an evaluation may bereceived from the driver (block 1510). The received evaluation (and, insome embodiments, telematics data regarding operation of the targetvehicle 202.1) may be transmitted to a remote server (block 1512), wherethe evaluation may be associated with the target vehicle 202.1 and/or avehicle operator of the target vehicle 202.1 (block 1514). The method1500 may be implemented continuously or at appropriate intervalsthroughout the duration of vehicle operation to facilitate evaluation ofa plurality of target vehicles 202.1, in some embodiments.

At block 1502, the mobile computing device 110 or on-board computer 114may monitor the operating environment of the vehicle 108 using sensordata from one or more sensors. This may include monitoring the absoluteor relative positions and/or movement of a plurality of other vehicles202 within the operating environment of the vehicle 108. The sensors andsensor data may include any sensors described herein or similar sensorsconfigured to generate or collect telematics data and/or other dataregarding an operating environment of a vehicle. In some embodiments,power-saving methods of monitoring the vehicle operating environment maybe implemented to limit the power used in environmental monitoring, asdescribed elsewhere herein. In further embodiments, the monitoring mayinclude (or may be limited to using only) sensor data collected forother purposes, such as sensor data collected for vehicle navigation,collision avoidance, autonomous or semi-autonomous operation of thevehicle 108, etc. Thus, the data collected to monitor the vehicleoperating environment may be collected from sensors or systems operatingto provide warnings to the driver or control some aspects of operationof the vehicle 108 (e.g., collision avoidance systems, adaptive cruisecontrol systems, automatic lane centering systems, etc.). In otherembodiments, the mobile computing device 110 or on-board computer 114may include sensor data collected particularly for use in evaluatingother vehicles 202 in the vehicle environment.

At block 1504, the mobile computing device 110 or on-board computer 114may identify a target vehicle 202.1 from among one or more vehicles 202within the operating environment of the vehicle 108. The target vehicle202.1 may be identified based upon a characteristic, condition, action,or movement of the target vehicle 202.1. For example, the target vehicle202.1 may be identified based upon a proximity to the vehicle 108, suchas a near collision within a collision threshold distance. As anotherexample, the target vehicle 202.1 may be identified by another negativedriving event, such as swerving between lanes, drifting onto a roadwayshoulder, hard braking, or excessive acceleration. As yet anotherexample, the target vehicle 202.1 may be identified based upon aninsurance policy associated with the target vehicle 202.1, such as byidentifying the vehicle 202.1 (e.g., using an image of a license plateor data received by V2V wireless communication) and matching the targetvehicle 202.1 with an insurance policy (e.g., by submitting a query to adatabase associated with an insurer providing insurance for the vehicle108 via the network 201 and external computing device 206). In someembodiments, the driver of the vehicle 108 may choose to identify thetarget vehicle 202.1 by providing an indication of the target vehicle202.1 via the mobile computing device 110 or on-board computer 114. Forexample, the driver (or a passenger) of the vehicle 108 may input acommand to identify and/or evaluate a nearby vehicle 202 via the mobilecomputing device 110 or on-board computer 114. One or more nearbyvehicles 202 may be presented to the driver for further selection of thetarget vehicle 202.1, or the mobile computing device 110 or on-boardcomputer 114 may separately identify each nearby vehicle 202.1-N as atarget vehicle for evaluation. In some embodiments, a plurality oftarget vehicles 202.1 may be identified, either sequentially orsimultaneously.

At block 1506, the mobile computing device 110 or on-board computer 114may record sensor data regarding the identified target vehicle 202.1from one or more sensors. The sensor data may include telematics orother data regarding the operation of the target vehicle 202.1, such aslocation, movement, path, proximity to other objects (e.g., the vehicle108, other vehicles 202, or infrastructure components 208), wirelesslytransmitted V2V communication data, or similar data regarding theoperation of the target vehicle 202.1 within the operating environmentof the vehicle 108. The data may be recorded as received from thesensors (e.g., full video from a camera, distance from the targetvehicle 202.1, etc.), in a processed form (e.g., determined speed ormomentum of the target vehicle 202.1), or in a summary form (e.g.,images or times associated with changes in direction or speed of thevehicle 202.1). In some embodiments, the recorded data may be storedlocally in a storage medium of the mobile computing device 110 oron-board computer 114, or some or all of the recorded data may betransmitted to an external computing device 206 via the network 201 inother embodiments. In further embodiments, the recorded sensor data maybe obtained from one or more sensors disposed within the target vehicle202.1, such as GPS units or accelerometer arrays.

At block 1508, the mobile computing device 110 or on-board computer 114may present an option to evaluate the operation of the target vehicle202.1 to the driver of the vehicle 108 or to a passenger of the vehicle108. The option to evaluate the operation of the target vehicle 202.1may be presented immediately upon identification of the target vehicle202.1, at a later point during operation of the vehicle 108, or afteroperation of the vehicle 108 (i.e., after completion of the currentvehicle trip). The option to perform an evaluation may be delayed untilthe conditions exist for safe evaluation by the driver, such as when thevehicle 108 has been parked and shut down. In some embodiments, theoption may be presented to the driver via an e-mail, text message, orpush notification from a Telematics App presented via the mobilecomputing device 110 or another computing device associated with thedriver. In further embodiments, the option may be presented in aTelematics App when activated by the driver. The option to evaluate theoperation of the target vehicle 202.1 may include one or more evaluationoptions indicating the driver's evaluation of the operation of thetarget vehicle 202.1. For example, the driver may be presented a set ofevaluation options numbered one through five, indicating a scale ofquality of operation. As another example, the driver may be presented aset of two evaluation options, one indicating proper vehicle operationand one indicating improper or unsafe vehicle operation. The option toevaluate the operation of the target vehicle 202.1 may further includeone or more evaluation options associated with actions of the targetvehicle 202.1 determined from the recorded data. For example, therecorded data may include a lane change of the target vehicle 202.1,which may be identified and presented to the driver of the vehicle 108for evaluation.

At block 1510, the driver or other user of the mobile computing device110, on-board computer 114, or other computing device may enter anevaluation of the operation of the target vehicle 202.1. The evaluationreceived by the mobile computing device 110, on-board computer 114, orother computing device may include one or more indications of aspects ofoperation of the target vehicle 202.1. Additionally, some embodimentsmay permit the vehicle operator to enter or record a free-formdescription of the operation (e.g., “he cut me off,” “they nearly ran usoff the road,” “they let me merge onto the highway,” etc.). Someembodiments may permit the driver to record a spoken description of theevaluation, such as when the evaluation occurs during ongoing operationof the vehicle 108. In some embodiments, an incentive may be offered tothe driver of the vehicle 108 to encourage entering an evaluation of thetarget vehicle 202.1, such as a credit or discount on an insurancepolicy associated with the vehicle 108 or the driver of the vehicle 108.

At block 1512, the received evaluation may be transmitted to an externalcomputing device 206 (such as a remote server) via the network 201. Theexternal computing device 206 may be associated with an insurer orthird-party driving evaluation system. The evaluation may be transmittedimmediately or transmitted at a later time, such as when the vehicletrip is complete. In some embodiments, the recorded sensor data (or asummary thereof) may also be transmitted together with the evaluation ormay be separately transmitted. In further embodiments, the evaluation oran indication of the general assessment contained in the evaluation(e.g., positive evaluation, negative evaluation, etc.) may betransmitted directly or via a remote server to the target vehicle 202.1to provide feedback regarding vehicle operation. For example, an alertmay be sent to a transceiver of the vehicle 202.1 indicating a negativeevaluation has been received, which may include an indication of a causeof the negative evaluation (e.g., failure to signal a lane change,excessive lane changing, etc.). Such evaluations may be transmittedanonymously or may be associated with the evaluating vehicle 108, driverof the vehicle 108, or other person performing the evaluation. In someembodiments, the evaluation or a warning based upon the evaluation maybe transmitted to other vehicles 202 within the operating environment ofthe vehicle 108 to alert other drivers to poor driving behavior by thedriver of the target vehicle 202.1.

At block 1514, the evaluation may be associated with the target vehicle202.1 and/or with a driver of the target vehicle 202.1. This may includedetermining the current operator of the target vehicle 202.1 ordetermining an owner, insured driver, or other person associated withthe target vehicle 202.1. The evaluation may be associated with thetarget vehicle 202.1 and/or a driver thereof by adding the vehicleoperation evaluation to a database with an indication of suchassociation. Alternatively, the evaluation may be used to update aprofile or score associated with the target vehicle 202.1 and/or adriver thereof, such as by adjusting a weighted score or adjusting alevel included within the profile. In further embodiments, theevaluation or profile may be used to warn other drivers and/or todetermine or adjust an insurance policy. For example, an insurancepolicy associated with the target vehicle 202.1 or the evaluated driverthereof may be revised based upon evaluations by other drivers (e.g., adiscount may be received for numerous positive evaluations or a discountmay be rescinded for numerous negative evaluations by other drivers).Such revisions may include changes to risk ratings, coverage amounts,coverage options, deductibles, premiums, discounts, surcharges, or otheraspects of the insurance policy. Such changes may be implementedimmediately or upon renewal of the insurance policy.

FIG. 16 illustrates a flow diagram of an exemplary vehicle alert method1600 for warning drivers of proximity to other vehicles 202 associatedwith negative evaluations. The method 1600 may begin by monitoring thevehicle operating environment of a vehicle 108 (block 1602). When thevehicle 108 is determined to be in proximity to another vehicle 202(block 1604), the other vehicle 202 may be identified (block 1606), andevaluation data associated with the other vehicle 202 may be obtained(block 1608). The evaluation data may be used to determine whether theother vehicle 202 is associated with a heightened risk due to negativeevaluations (block 1610). When the other vehicle 202 is determined topose a heightened risk (block 1612), an alert may be presented to thedriver of the vehicle 108 to warn the driver to use caution when drivingnear the other vehicle 202 (block 1614). In some embodiments, thevehicle 108 may further transmit a warning or the evaluation informationto other nearby vehicles. The method 1600 may be implementedcontinuously or at appropriate intervals throughout the duration ofvehicle operation to provide warnings regarding other vehicles 202through a vehicle trip of the vehicle 108.

At block 1602, the mobile computing device 110 or on-board computer 114may monitor the operating environment of the vehicle 108 using data fromone or more sensors. The sensors may include transceivers configured toreceive wireless V2V communication from other vehicles 202 or from smartinfrastructure 208 in the vehicle operating environment. For example,V2V data may be wirelessly received by the vehicle 108 indicating thepresence and/or identity of nearby vehicles 202. Other sensors mayinclude still image or video cameras, GPS units, or other sensorsdiscussed elsewhere herein. As discussed above with respect to block1502, monitoring the vehicle operating environment may include thepower-saving methods described elsewhere herein, may include (or may belimited to using only) sensor data collected for other purposes, or mayinclude sensor data collected primarily (or solely) for use in providingwarnings regarding other vehicles 202.

At block 1604, the mobile computing device 110 or on-board computer 114may determine another vehicle 202 is within proximity of the vehicle108. Determining another vehicle 202 is proximal to the vehicle 108 mayinclude determining that another vehicle 202 is operating within amonitoring distance threshold of the vehicle 108, which monitoringdistance threshold may depend upon the operating conditions (e.g.,limited access highway, residential street, heavy traffic, low traffic,clear weather, icy road conditions, etc.). In some embodiments, theproximity determination may include determining whether the vehicles 108and 202 are approaching or separating, or it may include determiningwhether the vehicles 108 and 202 are on the same or intersectingroadways. For example, a nearby vehicle 202 on a surface street thatdoes not intersect with a limited access highway on which the vehicle108 is traveling may be disregarded regardless of straight-line distancebetween the vehicles. In further embodiments, projected vehicle pathsmay be used to determine whether a vehicle 202 is operating withinproximity to the vehicle 108.

At block 1606, the mobile computing device 110 or on-board computer 114may identify the vehicle 202 operating in proximity to the vehicle 108.This may be accomplished using the sensor data received from the one ormore sensors at block 1602 or may be accomplished using additional data.For example, V2V data including a vehicle identifier may be receivedfrom a transceiver of the vehicle 202. As an alternative example, avehicle tag or license plate may be captured via a cameracommunicatively connected to the mobile computing device 110 or on-boardcomputer 114, which may be processed to identify the vehicle 202. Othermethods of vehicle identification as described elsewhere herein may alsobe used.

At block 1608, the mobile computing device 110 or on-board computer 114obtains evaluation data regarding the identified vehicle 202. Theevaluation data may be received from an external computing device 206via the network 201 in response to a request from the mobile computingdevice 110 or on-board computer 114. The external computing device 206may be a remote server associated with an insurer of the vehicle 108 orthe vehicle 202, or it may be a remote server associated with athird-party driver evaluation or rating agency. The evaluation data mayinstead be received directly from the vehicle 202, such as via wirelessV2V communication. As yet another alternative, the evaluation data maybe stored in a local database stored in a memory device associated withthe mobile computing device 110 or on-board computer 114, which may beupdated periodically to include evaluation data regarding a plurality ofvehicles operating in the geographic area in which the vehicle 108 isusually or currently operating. The evaluation data may be associatedwith the identified vehicle 202 or a driver of the identified vehicle202, as described above. The evaluation data may include scores,profiles, or other indications of risk associated with operation of thevehicle 202, such as those types of evaluation data described above. Insome embodiments, the evaluation data may include a risk levelassociated with the operation of the identified vehicle 202 (generally,or by a particular driver), which may be specific to the operatingconditions of the vehicle environment (e.g., weather, traffic, roadtype, time of day, etc.). In some embodiments, the evaluation data maybe received from a third vehicle operating within the same environmentas the vehicle 108 and the identified vehicle 202, such as from anothervehicle that has identified the vehicle 202 and obtained associatedevaluation data.

At block 1610, the mobile computing device 110 or on-board computer 114may determine whether the vehicle 202 is associated with heightened risklevels based upon the received evaluation data. In some embodiments,this may include determining a risk level or score within a receivedprofile that corresponds to the current conditions within the operatingenvironment (e.g., weather, traffic, road integrity, etc.). Determiningwhether there is a heightened risk level associated with the vehicle 202may further include determining whether the evaluation data indicatethat operation of the vehicle 202 in the current conditions (or by thecurrent driver, if identified) is associated with a risk level that isabove an alert threshold. Such alert threshold may be dependent uponconditions in the operating environment, or it may be determinedrelative to other vehicles in the operating environment (e.g., a risklevel one standard deviation above the norm for vehicles operating inthe geographic area of the vehicle 108). In some embodiments, thevehicle 202 may be determined to be associated with a heightened riskwhen the received evaluation data indicates that the vehicle 202 (ordriver thereof) has received more than a threshold number of negativeevaluations or has an evaluation score below a lower threshold. Suchmetrics may indicate that the vehicle 202 is typically operated in animproper manner or in a manner that results in increased risk to nearbyvehicles.

In some embodiments, an external computing device 206 may monitor thelocations of the vehicle 108 and other vehicles 202 using GPS or similardata. For example, a remote server associated with an insurer orthird-party warning system may monitor a plurality of vehicles in ageographic area. The external computing device 206 may identify eachvehicle and associate a driving evaluation score or profile with eachvehicle. Based upon such scores or profiles, the external computingdevice 206 may determine that one or more vehicles 202 are associatedwith increased risks to other nearby vehicles 108, so the externalcomputing device 206 may transmit indications of such risk to thevehicles 108 to cause alerts to be presented to the drivers of thosevehicles.

At block 1612, the mobile computing device 110 or on-board computer 114may determine whether it has been determined that the identified vehicle202 is associated with a heightened risk based upon evaluation data.When the identified vehicle 202 has been determined to be associatedwith a heightened risk, the mobile computing device 110 or on-boardcomputer 114 may generate and present a warning to the driver of thevehicle 108 (block 1614). This may include a visual, audible, or hapticalert, which may include information identifying the particular vehicle202. The alert presented to the driver of the vehicle 108 may includeinformation identifying the identified vehicle 202 in a manner easilyunderstandable by the driver, such as by relative position (e.g., aheadfifty feet in the right lane), description of the vehicle 202 (e.g.,blue sedan, red truck, etc.). In some embodiments, additionalinformation regarding the identified vehicle 202 may be presented, suchas a warning level (e.g., low, moderate, or high) or type of behaviortypically associated with such vehicle 202 (e.g., aggressive driving,lane departures, distracted driving, etc.). Such information may then beused by the driver of vehicle 108 (or an autonomous operation featurecontrolling part or all of the operation of the vehicle 108) to adjustoperation to limit exposure to the increased risk. Such actions mayinclude rerouting the path of the vehicle 108 around one or morevehicles 202 associated with heightened risk. For example, a navigationsystem may generate an alternative route if the previously determinedroute for the vehicle 108 will intersect with a sufficient number ofhigh-risk vehicles, similar to the way such navigation system may routearound anticipated heavy traffic areas (as described elsewhere herein).When the warning has been presented or other action has been taken, themethod 1600 may terminate. When the identified vehicle 202 has beendetermined not to be associated with a sufficiently heightened risk, themethod 1600 may also terminate. In either case, the method 1600 mayrestart at block 1602 until the vehicle 108 has completed a vehicletrip.

Pedestrian and Cyclist Warnings

In one aspect, systems and methods for warning drivers or passengers ofnearby pedestrians and/or cyclists may be provided. Such systems andmethods may monitor the environment near a vehicle to identifypedestrians or cyclists that may be approaching the vehicle or that thevehicle may be approaching. This may be particularly advantageous inwarning drivers or passengers not to open doors when bicyclists areapproaching at high speed from behind a parked vehicle.

FIG. 17 illustrates a flow diagram of an exemplary pedestrian warningmethod 1700 for alerting drivers to the presence of pedestrians orcyclists near a vehicle 108. The method 1700 may begin by monitoring thevehicle operating environment of the vehicle 108 for indications of thepresence of pedestrians and/or cyclists (block 1702). Sensor data orother data may be used to identify one or more pedestrians and/orcyclists within a threshold distance of the vehicle 108 (block 1704).Additionally, paths of the pedestrians and/or cyclists may be projectedto determine whether any of the pedestrians or cyclists will pass withinan alert threshold distance of the vehicle 108 (block 1706). When it isdetermined that at least one pedestrian or cyclist will pass within thealert threshold distance of the vehicle 108 (block 1708), an alert maybe presented to the driver of the vehicle 108 to warn the driver of thepresence of pedestrians and/or cyclists (block 1710).

At block 1702, a mobile computing device 110 or on-board computer 114may monitor the environment around the vehicle 108 for indications ofpedestrians, cyclists, or other objects moving within the vehicleoperating environment (e.g., wild or domestic animals). The mobilecomputing device 110 or on-board computer 114 may monitor theenvironment using sensor data received from one or more sensorscommunicatively connected thereto, including any of the sensor or typesof sensor data described elsewhere herein. In some embodiments, themobile computing device 110 or on-board computer 114 may monitor thevehicle environment based upon electronic signals received from devicescarried on or about the pedestrians or cyclists (e.g., mobile phones,wearable devices, fitness trackers, signal generators, etc.). Suchsignals may be directly or indirectly received by the mobile computingdevice 110 or on-board computer 114. For example, a smartphone carriedby a pedestrian may transmit a GPS location to a remote server (i.e.,external computing device 206) via network 201, and the mobile computingdevice 110 within the vehicle 108 may receive the GPS location of thepedestrian from the remote server via the network 201. As an alternativeexample, a smartphone carried by another pedestrian may transmit aBluetooth signal that may be directly detected by the mobile computingdevice 110.

At block 1704, the mobile computing device 110 or on-board computer 114may identify one or more pedestrians, cyclists, or other objects movingwithin the vehicle environment based upon the data collected. This mayinclude identifying the pedestrians, cyclists, or other objects arewithin a threshold distance of the vehicle 108, which threshold distancemay depend upon the speed at which the vehicle 108 is traveling. In someembodiments, only pedestrians or cyclists within the threshold distancein front of the vehicle 108 (i.e., within the threshold distance in thedirection of vehicle travel) may be identified. In other embodiments,pedestrians or cyclists behind the vehicle 108 may be identified if theyare moving faster than the speed of the vehicle. In instances in whichthe vehicle 108 is stopped, all pedestrians or cyclists within thethreshold distance may be identified. In alternative instances in whichthe vehicle 108 is stopped, only cyclists or pedestrians approaching thevehicle 108 faster than a minimum speed threshold may be identified. Forexample, slowly walking pedestrians may not be identified because theydo not present a significant risk, but a fast-moving bicyclist may beidentified because a significant risk of an accident may exist.

At block 1706, the mobile computing device 110 or on-board computer 114may determine whether the path of any of the identified pedestrians,cyclists, or other objects moving within the vehicle environment willpass within an alert threshold distance of the vehicle 108. This mayinclude determining an expected path of the vehicle 108, as well asprojected paths of one or more identified pedestrians or cyclists. Thealert threshold distance may depend upon the speed of the vehicle 108and/or the speed of the pedestrian or cyclist. In some embodiments, thealert threshold distance may approximate a distance a door of thevehicle 108 typically opens. For example, a stopped vehicle 108 may havean alert threshold distance of one meter to approximate the distance adoor of the vehicle 108 may open (thereby creating a risk of a collisionwith a pedestrian or cyclist passing near the vehicle 108). In someembodiments, the direction in which the pedestrian, cyclist, or otherobject may pass the vehicle 108 may be used to determine whether analert should be generated. In further embodiments, the mobile computingdevice 110 or on-board computer 114 may determine only those identifiedpedestrians, cyclists, or other objects that are expected to pass withinthe alert threshold distance of the vehicle 108 while within a roadway.Thus, pedestrians walking on a sidewalk may not be determined to bewithin the alert threshold distance for purposes of warning the driver,but pedestrians within a crosswalk may be determined to be within thealert threshold distance.

At block 1708, the mobile computing device 110 or on-board computer 114may determine whether at least one pedestrian, cyclist, or other objecthas been determined to be expected to pass within the alert thresholddistance of the vehicle 108 (and meet any further criteria applied).When at least one such pedestrian, cyclist, or other object has beendetermined to be present, at block 1710 the mobile computing device 110or on-board computer 114 may generate and present an alert to the driverand/or passengers of the vehicle 108. The alert may be audible, visual,haptic, or a combination of such alert types. In some embodiments, thealert may indicate a direction of the pedestrian, cyclist, or otherobject about which the driver and/or passengers are being warned. Thismay include an indication of a direction and/or distance. If no suchpedestrians, cyclists, or other objects have been determined to bepresent in the vehicle operating environment, the method 1700 mayterminate or may continue to monitor the vehicle operating environmentat block 1702. In some embodiments, the method 1700 may continuouslyoperate until the vehicle 108 has been parked and all passengers thereinhave left the vehicle 108.

Discounts & Risk Profile Based Upon Travel Environment

In one aspect, a computer system configured to generate a vehicle ordriver risk profile may be provided. The computer system including oneor more processors, sensors, and transceivers configured to: (1)receive, via wireless communication or data transmission over one ormore radio links, telematics and sensor data from a vehicle or a mobiledevice of an insured; (2) determine an average travel environment thatthe vehicle travels in from processor analysis of the telematics andsensor data, the average travel environment accounting for an averageamount of pedestrian traffic and an average amount of vehicle trafficthat the vehicle typically travels in; (3) use the average travelenvironment to build or model a risk profile for the insured or vehicle;(4) generate or update an auto insurance discount or a usage-based autoinsurance premium based upon their risk profile; and/or (5) transmit,via wireless communication or data transmission over one or more radiolinks, the auto insurance discount or the usage-based auto insurancepremium to the insured's vehicle or mobile device for display tofacilitate the insured's review and approval such that insurancediscounts are provided based upon a risk associated with the averagetravel environment that an insured vehicle or insured typically travelswithin. The system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

For instance, determining an average travel environment that the vehicletravels in from processor analysis of the telematics and sensor data mayinclude inputting the telematics and sensor data into a trained machinelearning program that determines an average travel environment, theaverage travel environment including or identifying (i) a level ofpedestrian traffic, and (ii) a level of vehicle traffic that the vehicletypically travels in. The sensor data may be collected or generated byone or more vehicle-mounted and front facing sensors, including one ormore of: a camera, video recording, infrared device, radar unit, orother sensors mentioned herein. The telematics and sensor data indicatesor includes information detailing (i) an amount of pedestrian traffic,and/or (ii) the types of streets that the vehicle travels through on adaily or weekly basis, and the risk averse profile reflects the amountof pedestrian traffic and/or types of streets. Each specific street(such as by name), or each type of street may include a risk or safetyrating, or a risk or safety rating for a given time of day or year—suchas different rating from daylight or nighttime, or rush hour, or forwinter (snow buildup) vs. summer.

The telematics or other data may indicate or include informationdetailing (i) an amount of vehicle traffic, and/or (ii) the types ofroads that the vehicle travels through or in on a daily or weekly basis,and the risk averse profile reflects the amount of vehicle trafficand/or types of roads. The telematics or other data may be collectedover one or more vehicle trips or days, and may indicate or includevehicle speed or average speed. The telematics and sensor data mayindicate a number of intersection or smart street lights orinfrastructure components that the vehicle travels through during adaily commute. The telematics and sensor data may include dataoriginally generated or collected by smart infrastructure or othervehicles. The usage-based insurance may be priced per miles traveled orby period of time.

In some embodiments, the telematics and/or sensor data may be input intoa machine learning program that has been trained to determine an averagetravel environment that the vehicle travels in from processor analysisof the telematics and sensor data. The machine learning program mayaccount for (i) an average amount of pedestrian traffic, and (ii) anaverage amount of vehicle traffic that the vehicle typically travels inwhen generating, building, or modeling the average travel environmentfor the vehicle. Additionally or alternatively, the telematics and/orsensor data, and/or the average travel environment determined may beinput into another machine learning program to build or model a riskprofile for (a) the insured, and/or (b) the vehicle.

In another aspect, a computer-implemented method of generating riskprofiles for vehicles may be provided. The method may include (1)receiving, by one or more processors or associated transceivers viawireless communication or data transmission over one or more radiolinks, telematics and sensor data from a vehicle or a mobile device ofan insured; (2) determining, via the one or more processors, an averagetravel environment that the vehicle travels in from processor analysisof the telematics and sensor data, the average travel environmentaccounting for an average amount of pedestrian traffic and an averageamount of vehicle traffic that the vehicle typically travels in; (3)using, via the one or more processors, the average travel environment tobuild a risk profile for the insured or vehicle; (4) generating orupdating, via the one or more processors, an auto insurance discount ora usage-based auto insurance premium based upon their risk profile;and/or (5) transmitting, via the one or more processors or associatedtransceivers via wireless communication or data transmission over one ormore radio links, the auto insurance discount or the usage-based autoinsurance premium to the insured's vehicle or mobile device for displayto facilitate the insured's review and approval such that insurancediscounts are provided based upon a risk associated with the averagetravel environment that an insured vehicle or insured typically travelswithin.

Determining, via the one or more processors, an average travelenvironment that the vehicle travels in from processor analysis of thetelematics and sensor data may include inputting the telematics andsensor data into a trained machine learning program that determines anaverage travel environment that includes, characterizes, or otherwiseidentifies (i) a level of pedestrian traffic, and (ii) a level ofvehicle traffic that the vehicle typically travels in. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein, and may be implemented via one or more localor remote processors, sensors, and transceivers executingcomputer-readable instructions stored on non-transitorycomputer-readable medium or media.

Vehicle Collision Cause Determination & Reconstruction

In one aspect, a computer system configured to determine causes ofvehicle collisions and reconstruct collisions may be provided. Thecomputer system may include one or more processors, sensors, ortransceivers configured to: (1) receive, via wireless communication ordata transmission over one or more radio frequency links, smart trafficlight data from a smart traffic light transceiver, the smart trafficlight data including time-stamped data associated with when the trafficlight was red, green, and yellow before, during, and/or after a vehiclecollision; (2) receive, via wireless communication or data transmissionover one or more radio frequency links, vehicle or mobile devicetime-stamped GPS (Global Positioning System) and speed data from avehicle or mobile device transceiver acquired before, during, and/orafter the vehicle collision; (3) compare the time-stamped smart trafficlight data with the time-stamped GPS and speed data to determine if thevehicle or another vehicle was a cause of the vehicle collisionoccurring at an intersection associated with the smart traffic light;and/or (4) update an insurance policy premium or discount based uponwhich vehicle caused the vehicle accident to facilitate not penalizingnot-at-fault drivers and generating insurance premiums or discounts morereflective of actual risk, or lack thereof, associated with certaintypes of vehicles and/or risk averse drivers. The system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

For instance, the one or more processors may be further configured togenerate a virtual reconstruction of the vehicle collision that includesa graphical representation of the traffic light changing. The one ormore vehicles involved in the vehicle collision may be autonomous orsemi-autonomous vehicles. The one or more processors may be furtherconfigured to generate a virtual reconstruction of the vehicle collisionwhich includes a time-lapsed graphical representation of the speed andlocation of the vehicle and depicts the traffic light changing.

The one or more processors or transceivers may be further configured toreceive telematics and sensor data indicating time-stamped actions takenby the vehicle or driver, and compare the time-stamped actions taken bythe vehicle or driver with the time-stamped smart traffic light data todetermine fault, or lack thereof, for the vehicle collision. The one ormore processors may be further configured to generate a virtualreconstruction of the vehicle accident which includes a time-lapsedgraphical representation of the speed and location of the vehicle thatis based upon the telematics and sensor data received from the vehicle,and visually depicts the traffic light changing. The sensor data may becollected or generated by one or more vehicle-mounted and front facingsensors, including one or more of: a camera, video recording, infrareddevice, or radar unit. The telematics data may include acceleration,speed, braking, and cornering information.

The one or more processors may further compare the time-stamped smarttraffic light data with the time-stamped GPS and speed data to (i)determine if the vehicle was traveling in accordance with the color ofthe smart traffic light at a time that a vehicle collision occurred atan intersection associated with the smart traffic light, or (ii)otherwise determine that the vehicle or driver did not cause the vehiclecollision. The time-stamped smart traffic light data, and telematics andsensor data received from the vehicle may be input into a machinelearning program that is trained to (i) determine if the vehicle wastraveling in accordance with the color of the smart traffic light at atime that a vehicle collision occurred at an intersection associatedwith the smart traffic light, or (ii) otherwise determine that thevehicle or driver did not cause the vehicle collision.

In another aspect, a computer-implemented method of vehicle collisioncause determination and vehicle collision reconstruction may beprovided. The method may include (1) receiving, at or by one or moreprocessors or associated transceivers via wireless communication or datatransmission over one or more radio frequency links, smart traffic lightdata from a smart traffic light transceiver, the smart traffic lightdata including time-stamped data associated with when the traffic lightwas red, green, and yellow before, during, and/or after a vehiclecollision; (2) receiving, at or by the one or more processors orassociated transceivers via wireless communication or data transmissionover one or more radio frequency links, vehicle or mobile devicetime-stamped GPS (Global Positioning System) and speed data from avehicle or mobile device transceiver acquired before, during, and/orafter the vehicle collision; (3) comparing, via the one or moreprocessors, the time-stamped smart traffic light data with thetime-stamped GPS and speed data to determine if the vehicle or anothervehicle was a cause of the vehicle collision occurring at anintersection associated with the smart traffic light; and/or (4)updating, via the one or more processors, an insurance policy premium ordiscount based upon which vehicle caused the vehicle accident tofacilitate not penalizing not-at-fault drivers and generating insurancepremiums or discounts more reflective of actual risk, or lack thereof,associated with certain types of vehicles and/or risk averse drivers.

The method may include additional, less, or alternate actions, includingthose discussed elsewhere herein, and may be implemented via one or morelocal or remote processors, sensors, and transceivers executingcomputer-readable instructions stored on non-transitorycomputer-readable medium or media. For instance, the method may includecomparing, via the one or more processors, the time-stamped smarttraffic light data with the time-stamped GPS and speed data to (i)determine if the vehicle was traveling in accordance with the color ofthe smart traffic light at a time that a vehicle collision occurred atan intersection associated with the smart traffic light, or (ii)otherwise determine that the vehicle or driver did not cause the vehiclecollision. The method may include inputting the time-stamped smarttraffic light data, and telematics and sensor data received from thevehicle into a machine learning program that is trained to (i) determineif the vehicle was traveling in accordance with the color of the smarttraffic light at a time that a vehicle collision occurred at anintersection associated with the smart traffic light, or (ii) otherwisedetermine that the vehicle or driver did not cause the vehiclecollision.

Electric Vehicle Battery Conservation

In one aspect, a computer system configured to perform accidentreconstruction for an electric or battery-powered vehicle, the computersystem comprising one or more vehicle-mounted processors, sensors, andtransceivers mounted on an electric vehicle, the one or more processors,sensors, and transceivers configured to: (1) receive or determine anindication of a trigger event from computer analysis of telematics orsensor data gathered by one or more sensors; (2) turn on a front-facingcamera or video camera mounted on the vehicle, the front-facing cameraor video camera configured to acquire or take images in front of, or tothe side of, a moving vehicle; and/or (3) transmit, via wirelesscommunication or data transmission, the image data associated with theimages acquired after the trigger event is detected to a remote serverfor computer analysis of the image data to facilitate not only accidentreconstruction and cause of loss determination, but to also facilitateconserving a battery powering an electric vehicle and only turning on acamera or video camera immediately prior to an anticipated or actualvehicle collision. The system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

The trigger event may be the one or more processors or sensors detectingvehicle speed unexpectedly or rapidly decreasing; detecting the vehiclefollowing distance unexpectedly or rapidly decreasing; detecting a brakepedal being engaged or otherwise triggered by brake system pressure orforce applied to the brakes being determined to be above a predeterminedthreshold; detecting vehicle deceleration above a predeterminedthreshold; detecting vehicle cornering to be above a predeterminedthreshold, or detecting that vehicle unexpectedly swerved given vehicleGPS location or direction of the road; detecting an animal in thevicinity of the vehicle; and/or other events. For instance, the triggerevent may be an infrared camera or radar unit detecting an animal in thevicinity of the vehicle, or the automatic deployment of a vehiclecollision avoidance system or feature, or the detection of thatautomatic deployment.

The one or more processors may be configured to receive or generatetelematics and sensor data from vehicle-mounted sensors, and input thetelematics and sensor data into a machine learning program that istrained to identify a trigger event potentially associated with, orassociated with, a vehicle collision, or trigger event that indicates ananomalous condition or a high risk of vehicle collision.

In another aspect, a computer-implemented method of vehicle collisioncause determination and/or reconstruction for an electric orbattery-powered vehicle may be provided. The method may include (1)receiving or determining, via one or more processors, an indication of atrigger event; (2) turning on, via the one or more processors, afront-facing camera or video camera mounted on the vehicle, thefront-facing camera or video camera configured to acquire or take imagesin front of, or to the side of, a moving vehicle; and/or (3)transmitting, via the one or more processors or an associatedtransceiver, the image data associated with the images acquired afterthe trigger event is detected to a remote server for computer analysisof the image data to facilitate not only accident reconstruction, but toalso facilitate conserving a battery powering an electric vehicle andonly turning on a video camera immediately prior to an anticipated oractual vehicle collision.

The method may include additional, less, or alternate actions, includingthose discussed elsewhere herein, and may be implemented via one or morelocal or remote processors, sensors, and transceivers executingcomputer-readable instructions stored on non-transitorycomputer-readable medium or media. For instance, the one or moreprocessors may be configured to receive or generate telematics andsensor data from vehicle-mounted sensors, and the method may includeinputting the telematics and sensor data into a machine learning programthat is trained to identify a trigger event potentially associated with,or associated with, a vehicle collision, or a trigger event thatindicates an anomalous condition or a high risk of vehicle collision.

Generating Vehicle-Usage Profile to Provide Discounts

In one aspect, a computer system configured to generate a driving scorefor individuals and build vehicle-usage profiles may be provided. Thecomputer system may include one or more processors, sensors, ortransceivers configured to: (1) detect or determine which individualwithin a household is driving a vehicle or sitting in the driver's seatat the outset of a vehicle trip by analyzing sensor data from one ormore vehicle-mounted sensors; (2) collect telematics data for thatvehicle trip; (3) assign or associate the telematics data for thatvehicle trip to the individual within the household that was identifiedas the driver during the vehicle trip; (4) determine a driving score forthe individual and vehicle trip based upon the one or more processorsanalyzing the telematics data for the vehicle trip; (5) update or builda vehicle-usage profile for the vehicle based upon the telematics datafor the vehicle trip or the driving score, the vehicle-usage profileindicating how much and what time of day each member of a householdtypically drives or uses the vehicle, and accounting for drivingbehavior of each driver within the household; and/or (6) update an autoinsurance premium or discount for the household or the vehicle basedupon the vehicle-usage profile to provide insurance cost savings tolower risk households and/or risk averse drivers. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

For instance, the one or more processors may be further configured totransmit via wireless communication or data transmission over one ormore radio frequency links, using a transceiver, the updated autoinsurance discount to the insured's mobile device for their reviewand/or approval. The one or more processors and sensors may be local tovehicle, such as mounted within a mobile device, and/or mounted on orwithin the vehicle or a vehicle controller. The one or more processorsmay be remote to the vehicle, such as a remote located server associatedwith an insurance provider. The insurance premium may be associated withusage-based auto insurance.

The sensor data may be input into a machine learning program trained todetect or determine which individual within a household is driving avehicle or sitting in the driver's seat at the outset of a vehicle tripfrom the sensor data. The sensor data may include data from weight,seat, or pressure sensors, image data from one or more cameras, or voicedata of the driver that is captured or generated by one or morevehicle-mounted sensors.

The one or more processors may detect or determine which individualwithin a household is driving a vehicle or is positioned to drive thevehicle (i.e., sitting in the driver's seat) at the outset of a vehicletrip by analyzing sensor data from one or more vehicle-mounted sensorsincludes the driver being authenticated by one or more of the following:PIN, voice recognition, facial scan, finger print scan, retina scan,authenticated key fob, and/or presence and/or identification of a mobiledevice (e.g., smart phone, smart watch, or wearable electronics). Thetelematics data may be input into a trained machine learning program todetermine a driving score for (i) the individual, and (ii) the vehicletrip. The telematics data may be input into a trained machine learningprogram to update or build a vehicle-usage profile for the vehicle thatindicates how much and what time of day each member of a householdtypically drives or uses the vehicle, and accounts for driving behaviorof each driver within the household.

In another aspect, a computer-implemented method of generating avehicle-usage profile may be provided. The method may include (1)detecting or determining, via one or more processors or sensors, whichindividual within a household is driving a vehicle or sitting in thedriver's seat at the outset of a vehicle trip by analyzing sensor datafrom one or more vehicle-mounted sensors; (2) collecting, via the one ormore processors or sensors, telematics data for that vehicle trip; (3)assigning, via the one or more processors, the telematics data for thatvehicle trip to the individual within the household that was identifiedas the driver during the vehicle trip; (4) determining, via the one ormore processors, a driving score for the individual and vehicle tripbased upon the one or more processors analyzing the telematics data forthe vehicle trip; (5) updating or building, via the one or moreprocessors, a vehicle-usage profile for the vehicle based upon thetelematics data for the vehicle trip or the driving score, thevehicle-usage profile indicating how much and what time of day eachmember of a household typically drives or uses the vehicle, andaccounting for driving behavior of each driver within the household;and/or (6) updating, via the one or more processors, an auto insurancepremium or discount for the household or the vehicle based upon thevehicle-usage profile to provide insurance cost savings to lower riskhouseholds and/or risk averse drivers.

The method may include inputting the telematics data into a trainedmachine learning program to determine a driving score for (i) theindividual, and (ii) the vehicle trip. The method may include inputtingthe telematics data into a trained machine learning program to update orbuild a vehicle-usage profile for the vehicle, the vehicle-usageprofiling indicating how much and what time of day each member of ahousehold drives or otherwise uses the vehicle on average, and accountsfor driving behavior of each driver within the household.

Machine Learning

As discussed above, a processor or a processing element may be trainedusing supervised or unsupervised machine learning, and the machinelearning program may employ a neural network, which may be aconvolutional neural network, a deep learning neural network, or acombined learning module or program that learns in two or more fields orareas of interest. Machine learning may involve identifying andrecognizing patterns in existing data (such as telematics data;autonomous vehicle system, feature, or sensor data; autonomous vehiclesystem control signal data; vehicle-mounted sensor data; mobile devicesensor data; and/or image or radar data) in order to facilitate makingpredictions for subsequent data (again, such as telematics data;autonomous vehicle system, feature, or sensor data; autonomous vehiclesystem control signal data; vehicle-mounted sensor data; mobile devicesensor data; and/or image or radar data). Models may be created basedupon example inputs of data in order to make valid and reliablepredictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as autonomous system sensor and/or vehicle-mounted, smartinfrastructure, or mobile device sensor data, and other data discussherein. The machine learning programs may utilize deep learningalgorithms are primarily focused on pattern recognition, and may betrained after processing multiple examples. The machine learningprograms may include Bayesian program learning (BPL), voice recognitionand synthesis, image or object recognition, optical characterrecognition, and/or natural language processing—either individually orin combination. The machine learning programs may also include naturallanguage processing, semantic analysis, automatic reasoning, and/ormachine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct or a preferredoutput. In unsupervised machine learning, the processing element may berequired to find its own structure in unlabeled example inputs. In oneembodiment, machine learning techniques may be used to extract thesensed items, such as driving behaviors or vehicle operation, generatedby one or more sensors, and under what conditions those items wereencountered.

Additionally, the machine learning programs may be trained withautonomous system data, autonomous sensor data, and/or vehicle-mountedor mobile device sensor data to identify actions taken by the autonomousvehicle before, during, and/or after vehicle collisions; identify whowas behind the wheel of the vehicle (whether actively driving, or ridingalong as the autonomous vehicle autonomously drove); identify actionstaken by the human driver and/or autonomous system, and under what(road, traffic, congestion, or weather) conditions those actions weredirected by the autonomous vehicle or the human driver; identify damage(or the extent of damage) to insurable vehicles after aninsurance-related event or vehicle collision; and/or generate proposedinsurance claims for insureds after an insurance-related event.

Additional Considerations

With the foregoing, an insurance customer may opt-in to a rewards,insurance discount, or other type of program. After the insurancecustomer provides their permission or affirmative consent, an insuranceprovider telematics application and/or remote server may collecttelematics and/or other data (including image or audio data) associatedwith insured assets, including before, during, and/or after aninsurance-related event or vehicle collision. In return, risk aversedrivers, and/or vehicle owners may receive discounts or insurance costsavings related to auto, home, life, and other types of insurance fromthe insurance provider.

In one aspect, telematics data, and/or other data, including the typesof data discussed elsewhere herein, may be collected or received by aninsured's mobile device or smart vehicle, a Telematics Applicationrunning thereon, and/or an insurance provider remote server, such as viadirect or indirect wireless communication or data transmission from aTelematics Application (“App”) running on the insured's mobile device orsmart vehicle, after the insured or customer affirmatively consents orotherwise opts-in to an insurance discount, reward, or other program.The insurance provider may then analyze the data received with thecustomer's permission to provide benefits to the customer. As a result,risk averse customers may receive insurance discounts or other insurancecost savings based upon data that reflects low risk driving behaviorand/or technology that mitigates or prevents risk to (i) insured assets,such as vehicles or even homes, and/or (ii) vehicle operators orpassengers.

Although the disclosure provides several examples in terms of twovehicles, two mobile computing devices, two on-board computers, etc.,aspects include any suitable number of mobile computing devices,vehicles, etc. For example, aspects include an external computing devicereceiving telematics data and/or geographic location data from a largenumber of mobile computing devices (e.g., 100 or more), and issuingalerts to those mobile computing devices in which the alerts arerelevant in accordance with the various techniques described herein.

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothingrequires that the operations be performed in the order illustrated.Structures and functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location, while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One may be implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

Those of ordinary skill in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

What is claimed is:
 1. A computer-implemented method of generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile, the method comprising: detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle; generating, via the one or more processors associated with the first vehicle, an electronic message regarding the abnormal traffic condition; transmitting, via a vehicle-mounted transceiver associated with the first vehicle, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition; receiving, via the one or more processors associated with the first vehicle, telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and transmitting, via the one or more processors associated with the first vehicle, the telematics data to a remote server, wherein the remote server updates a vehicle-usage profile associated with the nearby vehicle.
 2. The computer-implemented method of claim 1, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
 3. The computer-implemented method of claim 1, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
 4. The computer-implemented method of claim 1, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
 5. The computer-implemented method of claim 1, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
 6. The computer-implemented method of claim 1, wherein the transmitting the electronic message to the nearby vehicle requires transmitting the electronic message to one or more remote processors.
 7. The computer-implemented method of claim 1, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
 8. The computer-implemented method of claim 1, wherein the nearby vehicle travels to the operating environment of the first vehicle.
 9. The computer-implemented method of claim 1, the method further comprising transmitting the electronic message to a smart infrastructure component, wherein the smart infrastructure component: analyzes the electronic message to determine a type of anomalous condition for the abnormal traffic condition; and performs an action based on the type of anomalous condition in order to modify the anomalous condition.
 10. The computer-implemented method of claim 1, wherein the electronic message contains location information of the abnormal traffic condition, and wherein the nearby vehicle ignores the electronic message when the location information indicates that the abnormal traffic condition is beyond a threshold distance from the nearby vehicle.
 11. A computer system configured to generate a vehicle-to-vehicle traffic alert and update a vehicle-usage profile, the computer system comprising one or more processors, the one or more processors configured to: detect that an abnormal traffic condition exists in an operating environment of a first vehicle; generate an electronic message regarding the abnormal traffic condition; transmit, via a vehicle-mounted transceiver associated with the first vehicle, the electronic message to a nearby vehicle, wherein the electronic message is transmitted via wireless communication to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition; receive telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message; and transmit the telematics data to a remote server, wherein the remote server updates a vehicle-usage profile associated with the nearby vehicle.
 12. The computer system of claim 11, wherein the abnormal traffic condition is one or more of the following: an erratic vehicle, an erratic driver, road construction, a closed highway exit, slowed or slowing traffic, slowed or slowing vehicular congestion, or one or more other vehicles braking ahead of the first vehicle.
 13. The computer system of claim 11, wherein the abnormal traffic condition is bad weather and the electronic message indicates a GPS location of the bad weather.
 14. The computer system of claim 11, the system further configured to generate an alternate route for the nearby vehicle to take to avoid the abnormal traffic condition.
 15. The computer system of claim 11, wherein updating the vehicle-usage profile causes an insurance premium adjustment to an insurance policy associated with an operator of the nearby vehicle.
 16. The computer system of claim 11, wherein the one or more processors is one or more of the following: vehicle-mounted sensors or vehicle-mounted processors.
 17. The computer system of claim 11, wherein the nearby vehicle comprises one or more of the following: an autonomous vehicle, a semi-autonomous vehicle or a self-driving vehicle, and wherein the nearby vehicle includes one or more processors for receiving the transmitted electronic message.
 18. The computer system of claim 11, wherein the transmission of the electronic message to the nearby vehicle requires transmission of the electronic message to one or more remote processors.
 19. The computer system of claim 11, wherein the abnormal traffic condition is detected by analyzing vehicular telematics data.
 20. The computer system of claim 11, wherein the nearby vehicle travels to the operating environment of the first vehicle. 